{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>141</td>\n",
       "      <td>920300</td>\n",
       "      <td>37141920300</td>\n",
       "      <td>9203</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>236115472</td>\n",
       "      <td>605423</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-78.15648 34.67909, -78.15458 34.680...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>141</td>\n",
       "      <td>990100</td>\n",
       "      <td>37141990100</td>\n",
       "      <td>9901</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "      <td>133298393</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>POLYGON ((-77.71433 34.29117, -77.71406 34.291...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>071</td>\n",
       "      <td>031600</td>\n",
       "      <td>37071031600</td>\n",
       "      <td>316</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>22744042</td>\n",
       "      <td>66304</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-81.32560 35.26363, -81.32556 35.263...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37</td>\n",
       "      <td>071</td>\n",
       "      <td>031800</td>\n",
       "      <td>37071031800</td>\n",
       "      <td>318</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>6071060</td>\n",
       "      <td>8149</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-81.25719 35.26613, -81.25585 35.266...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37</td>\n",
       "      <td>155</td>\n",
       "      <td>961801</td>\n",
       "      <td>37155961801</td>\n",
       "      <td>9618.01</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>103973789</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((-79.31012 34.59978, -79.31002 34.599...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20   NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        37        141    920300  37141920300     9203  Census Tract   G5020   \n",
       "1        37        141    990100  37141990100     9901  Census Tract   G5020   \n",
       "2        37        071    031600  37071031600      316  Census Tract   G5020   \n",
       "3        37        071    031800  37071031800      318  Census Tract   G5020   \n",
       "4        37        155    961801  37155961801  9618.01  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20    ALAND20   AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S  236115472     605423  ...       12        0       12        0   \n",
       "1          S          0  133298393  ...        0        0        0        0   \n",
       "2          S   22744042      66304  ...        0        0        0        0   \n",
       "3          S    6071060       8149  ...        0        0        0        0   \n",
       "4          S  103973789          0  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        0        0        0        0   \n",
       "1        0        0        0        0        0   \n",
       "2        0        0        0        0        0   \n",
       "3        0        0        0        0        0   \n",
       "4        0        6        0        0        6   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-78.15648 34.67909, -78.15458 34.680...  \n",
       "1  POLYGON ((-77.71433 34.29117, -77.71406 34.291...  \n",
       "2  POLYGON ((-81.32560 35.26363, -81.32556 35.263...  \n",
       "3  POLYGON ((-81.25719 35.26613, -81.25585 35.266...  \n",
       "4  POLYGON ((-79.31012 34.59978, -79.31002 34.599...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 8/13/22 - NOTE: VS. STD HD1, IN THIS SPECIAL CODE WE DIVIDE PRECINCTS INTO BLOCK GROUPS (A LA NJ) TO COMPARE TO STD CODE\n",
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/nc_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3e4af1be-0f6a-4c46-9d23-da70d63c30e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 2672 popn tracts for NC\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"NC\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "c665cf72-eb97-4990-a3b4-3f6a8a6f0611",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now convert Census county IDs to NC county numbers, numbering 1 to max\n",
      "the min and max county numbers I found were 1 100\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now convert Census county IDs to NC county numbers, numbering 1 to max\")\n",
    "doubleCountyNo = tractPopFile['COUNTYFP20']\n",
    "tractCountyNo = [-999]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCountyNo[t] = int( (int(doubleCountyNo[t])+1)/2 )  #Census captures county numbers as odds, skipping evens\n",
    "print(\"the min and max county numbers I found were\",np.min(tractCountyNo),np.max(tractCountyNo) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population NC voter-age population\n",
      "state pop= 10439388 VAP pct Hispanic is  0.0888169475318448\n"
     ]
    },
    {
     "data": {
      "image/png": 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VrfKOcz3wBNASl+hsanvTeDa976Y5uWwGdDrC72iM8Y24Dk/NR1FRkS5evNjvMEymUoXSdW79EICyvZDXxteQjGkMIlKsqkWxXktoQKKIZIlIdxHpFfpJbojGZICqSph9Mzx4Cuxc48osiRiT0AqJPwT+FzcDcNArVuCYFMZlTHop2wPPTYTVb8ApN0P7Pn5HZEzaSKSx/UbgSFXdkepgjElLuz93XXu3roBz/wpDJ/odkTFpJZFEshHYnepAjElbCx+E0vVw2XNwxEi/ozEm7SSSSNYAb4nIK0BZqFBV/5SyqIxJB5VlkJ0HI2+HIROsZ5YxtUiksX0DbjR7LpAf8WNM0/Xew3D/cNi3HbJyLIkYE0ciI9t/2RiBGJMWglUw53aYfy8MOBuyW/gdkTFpL5FeW52Bn+CmcQ//Vanq6SmMy5jGV74fZn0PPpkNJ14Lo34Lgay69zOmmUukamsy8AnQF/glsA5YlMKYjPHHnF/AJ6/AqLvg27+zJGJMghJpbO+oqo+KyI2q+jbwtoi8nerAjGl0p/0MBoyC/mf6HYkxGSWRO5IK7/dmEfmOiBxP9Vl3jclca96G6ZdDZTm07mhJxJh6SOSO5P9EpB3wY9zsum2Bm1MalTGN4f3J8PKPoGN/t6ZIfle/IzImIyXSa2u293A38M3UhmNMI1CFN38N837vVjMc+xS0aOd3VMZkrDqrtkSkn4i8LCLbRWSriLwoIv0aIzhjUuL1n7skcvwVbgp4SyLGNEgiVVtTgPuAC73n44CpwEmpCsqYlDp2PLTuDCNutCVxjUmCRBrbRVWfVtVK7+cZalkb3Zi0teMzmPcHV6112GA45SZLIsYkSSJ3JG+KyC3ANFwCuQR4RUQ6AKjqzhTGZ0zDbVjolsQFOO4yaNvN33iMaWISSSSXeL+vjSq/CpdYrL3EpK9ls+D566BdoZu915KIMUmXSK+tvo0RiDFJ9+698Ppt0Gs4jJsCrTr4HZExTVIivbbGiEi+9/jnIjLLG5RoTHor6A1fGwtXvGBJxJgUSqSx/RequkdETgHOAp4EHkxtWMbU04Hd8Onr7vHR58JFD0OOzeBrTColkkiqvN/fAR5Q1Rdxa5MYk152bYTHRsGzV8LerX5HY0yzkUgi+VxE/g6MBf4hInkJ7mdM49n0PjwyEnaXwKXToE0XvyMyptlIJCGMBV4DRqnqLqAD8D+pDMqYQ7LyVXj825CVB1e/7qY9McY0mlp7bYlIW1X9EreY1VteWQfcuu2LGyU6YxKx+SPofCSMn24TLxrjg3jdf6cA5wDFuPEikcOAbfyI8VewCkrXQcfD4dSfuOlOrFHdGF/UmkhU9Rzvt40jMemlfB/MvAY2zIcfLIbWnSyJGOOjeFVbQ+LtqKpLkh+OMXXY8wVMuQS++AhG3e2SiDHGV/Gqtv4Y8XgoroorRIHTUxKRMbXZ8jFMGQv7d8C4qXDkKL8jMsYQv2orvIiViLwf+dwYX8y/F6oq4LuvQvfj/I7GGONJZNJGsGnjjZ/K90NuK/jOH2H/TmjXw++IjDERfBtYKCJZIvK+iMz2nncQkTkissr7XRCx7a0islpEVorIWRHlQ0VkqffaPSK2wESTEgzCG790Aw0PfAk5LS2JGJOG4jW2/42DdyKFInJP5Ouq+qMGnvtGYAXQ1nt+CzBXVe/y1j+5BfipiAzErco4COgOvCEiA1S1CngAmAQsAP4BjAJebWBcJh1UHIAXrofls2DoRJdEjDFpKV7VVuSgw+Jat6oHESnEzd31a+C/vOLzgdO8x0/iBkH+1CufpqplwFoRWQ2cKCLrgLaqOt875lPABVgiyXz7dsC08bBxIZzxS1sS15g0F6+x/ckUnvcvwE+A/Iiyrqq62Tv3ZhEJTZbUA3fHEVLilVV4j6PLaxCRSbg7F3r16pWE8E1Kzb4RNn0AY56AQRf6HY0xpg6N3kYiIucAW1U10bucWF9Fo0faR5bXLFR9SFWLVLWoc+fOCZ7W+GbU3TBxtiURYzKEH43tI4DzvKqpacDpIvIMsEVEugF4v0PzgJcAPSP2LwQ2eeWFMcpNJvroOXjuu27qk3Y9oOeJfkdkjElQIiskjkikLFGqequqFqpqH1wj+r9U9XLgJWCCt9kE4EXv8UvAOBHJE5G+QH/gPa8abI+IDPN6a10ZsY/JFKrw9u9h1jWwdwtU7Pc7ImPMIUrkjuRvCZY11F3AmSKyCjjTe46qLgeeBT4G/gnc4PXYArgeeARYDXyGNbRnlqoKePEH8Ob/wTGXwBXPQ15+3fsZYw5Z8fpS7ntzNcXrS5N+7Hjdf4cDJwOdReS/Il5qC2Ql4+Sq+hbeFPWqugMYWct2v8b18IouXwwMTkYsxgfPXwvLZsKpt8Bpt1jPLGNSpHh9KZc9soDyyiC52QEmXzOMob0L6t4xQfG6/+YCbbxtIr8mfglcnLQITPN10nVwxJlw3Hi/IzGmSVuwZgfllUGCChWVQRas2dE4iURV3wbeFpEnVHV90s5omrfPi2H9u3DyD12DujWqG5Nyw/p1JDc7QEVlkJzsAMP6dUzq8ROZa+sRERnjLbOLN3XJNFU9K/5uxkRZMdutI9Kmixutbu0hxjSKob0LmHzNMBas2cGwfh2TejcCiSWSTqEkAqCqpRGDBY2pmyoseABe+xn0GArjp1kSMaaRDe1dkPQEEpJIr62giISHg4tIb2w2YHMoXrsNXrsVjj4XJrwMbWxQqDFNSSJ3JLcB/xaRt73n38CbbsSYhHQd6NpEzrgTAr5NOG2MSZE6E4mq/tNbdncYblqSm1V1e8ojM5nty82wdTkccQYcf7nf0RhjUijRha2qcFOWtAAGigiqOi91YZmM9sUytyRuZRnc+CHktfE7ImNMCtWZSETkGtzaIYXAB7g7k/nYmu0mltVvwLMTXWP6Fc9bEjGmGUikwvpG4ARgvbdu+/HAtpRGZTLT4sdg8lgo6APXvAHdjvE7ImNMI0ikauuAqh4QEUQkT1U/EZEjUx6ZyTzbPoXDT4cxj1v3XmOakUQSSYmItAdeAOaISCk2XbsJqfgKvtwEHQ+Hs37txoxkJdr0ZoxpChLptRVaXegOEXkTaIebhdc0d/u2w9TxLpH8YBHktvI7ImOMD+ImEhEJAB+p6mAIz79ljKvGmjIG9nwBox+2JGJMMxY3kahqUEQ+FJFeqrqhsYIyaW7dv2HaZZCVAxNfgcIivyMyxvgokcrsbsByEXkP2BcqVNXzUhaVSW//ucdNvHjZc66HljGmWUskkfwy5VGY9KcK5Xtdb6yLHgYNQsvUTABnjMksiSSSb6vqTyMLRORuwNpLmovKcnj5R7BjtavKatHO74iMMWkkkQGJZ8YoOzvZgZg09VUpPDMaPpwK/b8FWbl+R2SMSTPx1my/Hvg+0E9EPop4KR/4T6oDM2mgdB1MHuN+j34Yjhnrd0TGmDQUr2prCvAq8FvglojyPaq6M6VRGf+pwqxrYe9WuOIF6DPC74iMMWkq3prtu4HdwPjGC8ekBVUQgQvud43qnfr7HZExJo3ZKkPmIFXXtffFH7jHHQ9PeRIpXl/KfW+upnh9aUrPY4xJHZsUyThVlfDq/7gZfAdeAFUVkJ3ahvXi9aVc9sgCyiuD5GYHmHzNsJStKW2MSR27IzFQtgemjnNJZMRNcPHjKU8iAAvW7KC8MkhQoaIyyII1O1J+TmNM8tkdSXOnClMugQ0L4Ny/wtCJjXbqYf06kpsdoKIySE52gGH9OjbauY0xyWOJpLkTga//GFC3vnojGtq7gMnXDGPBmh0M69fRqrWMyVCWSJqrT1+HXevhxO/BESN9C2No7wJLIMZkOGsjaY4WPQJTL4H3n3GN6sYY0wB2R9KcBIMw5xcw/14YMAouetRNBW+MMQ3Q6HckItJTRN4UkRUislxEbvTKO4jIHBFZ5f0uiNjnVhFZLSIrReSsiPKhIrLUe+0eEZHGfj8ZQxVmTHRJ5MRJMG4K5LXxOypjTBPgR9VWJfBjVT0aGAbcICIDcdOwzFXV/sBc7znea+OAQcAo4H4RyfKO9QAwCejv/YxqzDeSUUSg5zAYdRd8+/cQyKp7H2OMSUCjJxJV3ayqS7zHe4AVQA/gfOBJb7MngQu8x+cD01S1TFXXAquBE0WkG9BWVeerqgJPRexjQrathLXz3OPh34dh1/sbjzGmyfG1sV1E+gDHAwuBrqq6GVyyAbp4m/UANkbsVuKV9fAeR5fHOs8kEVksIou3bduW1PeQ1tbOg0fPhNk3u5HrxhiTAr4lEhFpA8wEblLVL+NtGqNM45TXLFR9SFWLVLWoc+fOhx5sJvpgCjx9IeR3hyuehyzrV1EXm/fLmPrx5eoiIjm4JDJZVWd5xVtEpJuqbvaqrbZ65SVAz4jdC4FNXnlhjPLmTRXe+i28fTf0PRXGPgUt2/sdVdqzeb+MqT8/em0J8CiwQlX/FPHSS8AE7/EE4MWI8nEikicifXGN6u951V97RGSYd8wrI/Zp3nZthOMuh8tmWBJJkM37ZUz9+XFHMgK4AlgqIh94ZT8D7gKeFZGrgQ3AGABVXS4izwIf43p83aCqVd5+1wNPAC1xi3C92kjvIf3s3wkHdkOHvnDe31yvrCbUG7p4fSkL1uygoFUupfvLkz6lis37ZUz9ievw1HwUFRXp4sWL/Q4juXaucUviZuXCdf9ucl17I6udguoax/Jykl/9FEpWNu+XMTWJSLGqFsV6zaZIyXQbFsIjZ7g7ku/8qcklEahe7QSuR0Wi1U+JNKCHtgG44ZtHNCiJWIO9aY6sK08mW/68W1e9XQ/XHtLxcL8jSolQtVMomQQgoeqnyDuZ7IAwpqgno4cUVksUyWxkT1WDvd0pmXRniSRTBYMw/37ofryb7qR1etTpp+KiFzndfKw2ktrOGXknU16lTFm4gZlLSqpd4GM1stc37tqO1ZDPxHqTmUxgiSTTVFVA5QHIy4dLp0NOK8hp4XdUQGoverVNNx/vnKE7mbKKIEr1KrHoberTyB6dIGIdq6GfSTITnTGpYokkkxzYDc9NdGNFLp8FrTr4HVE1flz04p0zdCczc0kJM4pLqKqqmSyG9i7g9nMG8eqyzZw9uFvC8daWIKIX6rrvzdUN+kz86k1m1WnmUFgiyRS7NsKUsbD9UzjnzxBIr34SxetL2bTrK7IDQqXXKv7hxl0Ury9N6YUo3oU2dDG8aEghFw0pjHlhLF5fyp2zl1NeGWTRup0ceVh+QvHWlsCi75xqu0tJ9CLtxyqSVp1mDpUlkkyw6X2YMg4q9rtG9cO/6XdE1RSvL2X8Q/OpqFKX3xSqgNc/3sJbn25j6vdSdyGq7UIb62J4wzePqLF/dEKYtaQkoYt2QatcAiKAxr1TiI4POOSLdGOvImnVaeZQWSJJd8EqmHmNW4Dqytehy9H1OkyyqyoijzdzSQnlVe4upCpYfbtYF+dkxBJ9jOjjVGtorwzylzc+5aYzBtRooC9olRu+Y8jKCvDc4o1UBjXuRT50F1MVVLICwu3nDIr7PiLji6zqihVXOrDBmeZQWSJJZ6puXMjYp6FVR8jvWq/DJKuqIvLiG6oOys0O8I3+1SfCDAChfJKdJdUuzrefM6javvWJJfr9TBzeh+Wbv+Tswd249KRegLsYZgeEiiolqPDOqu38Z/V2Jn29H2cOOqza/refM4jS/eV8vusrpr23oc5v4qEkpYCqUrq/PObnFCtRRndl/veq7SxatzOtqo/8qE4zmc0SSToKVsFrt4EEYNRvoOvABh0uGVUVkRfvgAhB1fDxOuXnVfsGe8e5g1i2aTeu4odqF+fpizaEe1GVVQSZuaTkkGOJfD9lFUEenLcGcMkCCCcTRNCICaGDCg/OW8Nn2/dV+zxK95dzwzePoHh9KTMWb6Siyt1p1PZNPJwMKoKICAWtcmN+TrESZegi/Zc3PuXfq7bH7EmWDhq7Os1ktvRqsTVQvg+mXwELHwANuruSBgpd+LKk+kC+REdhF68v5S9vfBq++AaDSkAkfLyLhhRyx7mDOKV/J+44dxCXntSL31z4NX594dcY3L0dARECAllZAZZv2h2+tCswo7gk4VHgoXhD1VFZUnM6sVeXbQZcsqmMrmfzbP3yQLXPo6BVLve9uZqVX+w5eMA485SFenoFAi6h3jl7efg9JDL549DeBdx0xgDycmr+mxzK52Cj5026sDuSdLJnC0y9BDZ/CGf/Dk66NimHjVVVkWh1V2i70F1EQKhWnTSoW9tw99rKquo9n6LbEk4d0Jm5K7ZUO35VVWLfxqPjDVVH7fmqInxHAnD24G6AS56hO6dow/t15PZzB9WopguIUBV09zB1xVW6vzy8bVnFwbaORNsX6lt9ZD2qTDqyRJIuqirgie/Al5+7kepHnp3Uw0dXVSRa3RXZHhAARhzRibMHd+PO2cspqwiGq5NCyipc4/rQ3gXMXFJycDCgKl3y88gOSLhhXnB3KbG67MYbpR5ZHQXQq2Pr8DiQULXW0N4FnHNMN1744OASNaGqtifmr+PMQYdxwzePqNb4DS7hqdbsiRUdV0Gr3Gp3VpFtHYkmiPpUH6W6R1Xk+wydz9pJTF0skaSLrBw44w5oVwjdj0vZaSIbzEON0SKwaddXMcd8RH/DvumMAdWSS7RQm0h+XjbTF20MbyMCg7q34zkpodpClhF3DLG+bQM1eldFX+QvPanXwXYRz5SFG3jpw9jrnEVegKPfX+hOp66uxKX7ywkIMSeSbOjEj/GkskdV9NxkiFBZZXc+pm6WSPy25GnXM+u4S+Hoc1J6qugLRWjakMogTH3PzUMVfSGtrQqmtmojgCqFv89bUy3RKMLyTburtVsoUBXUcDtCZDtMqNvwzCUlNaqz6vqGXLy+lF+8sDR8kQfICrg2napg9buNuubxgth3AaFxJKoarvKr7cKezK7X8arEGnqeau+zyv3vSNfOACa9WCLxSzAIb/4fvPNHGDAKjh1frYE3FVNU1LxQRISjUF4R5PYXlxHU+OMohvYu4M7zB3P7i8vC7QTRosuqgsrCNTvIzgpQWRkkyMGLb0Gr3GrtMOC6D6/asqda76yH5n3GpG8cXufnMXNJCZFvT4DTj+rC4Z1ah7sJR/ekAmK2wQzr17HGXUCoXSWorirsmlP6kt8yJ+a/VSraNGJViSXjPJHvM8u7I4k1rUw86Ti1SjrG1NRYIvFDxQF48fuwbCYMmQDf+WONJJLMqc1Df0SxLhSRF3WB8PQm5RUHexyFLvJZAeHO8wdXq0qavmgD63fsZ9dXFXXGsnrbPrIDMP6kXuTnZTN/zQ66tm3Bsk27a1SVqcJ760rJ8kbKK7Bux35+9vxS4GAX31h1+tv3lFU7rwi88fEW5njvc9G6nQDV7j6iBzBGJ9TIu4DIbQUNJ5HQ51WftqiGSsZ5Yo3CP5QLcDp2BEjHmJoiSySNrbIcnr4ANsx3bSIjbqrR1TRZF5/i9aWMf3hB+Jv01O8Ni3mhKGiVy7JNu5m+aEP4ViII7PmqgllLSjhQ4aqjKoPKz19wF/IjD8vnjpeWhRvOE1UVdKd47D9rvX13E5DaezkHY/TgfWjeZxx5WD5AxNgWV30WDCpZAcLtFxLRjgHu8YGKID9/fikIZGcFuHhoIYO7twsnWeTgfGHR7R7F60v5fNdXZGcFqKpyyfXDjbv405yVVAXdAMzpk4YnNLtwMhu2k9V2En23k+kzFadjTE2RJZLGlp0Lh58OJ34PBl8Uc5NkXRT+/vZnlFe6K3G51+bw6wu/FvNCcdvzS2tMb/LQO2tq1FEFFW5/cRljT+hZo3osUau37KmWgIJxDhPrpfU79nPZIws4pke7cJIL9boC1+YT3r+2BOVtXl4ZZOrCDeRkBzh1QGd27y9n0bqD4zMiByZGdoUOiPvsPijZzesfH+zSXFml/HTGh9x98bHhz3b0kELE+x2rAT87KwCq1Ub/H+q69LHuJu57c/UhJ6aGVAOl49Qq6RhTU2RrtjeW9fNdo3rPExPa/FD+oKO3LV5fyoNvf8YbH2+pdiEODRQM7TNrSQkKDO7e7pDvLjq0zmH3/grqmUsaLNSVN9VO7FPAs9edDLgL8x9eW3mwJ5r3O1YcLXLiTwfzs+eXMnXhBjTqOAEID3RM1hQyiR4jGdVAsf4v+t0+kQ4xNAXx1my3O5LGsHQGvHA9dB8CV/0z7qjpkHhjDKKrRKLnnXpo3hqia4SyxCWM+95czcI1O5gXMf4jAAevZgnaua/uNpGUaqRMUrxhF1MWbggPfpSIarh4py+vdJ0DalsxcUZxSXj/rCwhgOuQIBHTz5RXJDapY/SFsr7VOclqZ4nXbdqPC7lN95J6lkhSSdX1yvrXr6DXyTBuckJJJJ7oP87RQwrDf/wHIuadihQQ+N7X+3Hn7OXhqqBIoWqeTJKsPNK2RTZfHqis9fVgUMMN77Gq4AQ4pX8nOrbOZcGaHWzbW4aqq2pbt2M/QHh6mM93fcWUhRt4ddnmcDdoAS7x1pKvNtK+wnWC+M/qgwMdoWY7SqyLdX2rc5JdDWTtE82HJZJUqaqA2TfB+8/A18bC+fdCdl6DDxv9xym4KUtiJYiQSV/vx5dllXG3yTSux1T1ZJIVgHYtc9m5r7y23WqIl0QgNM6mZgYR7yc3JxAe6R8an3N097Z8VLI7XFX1tR7tWL75S6Ys3FDjGHk5gXDbSegie+Rh+fzljU/5z+rt4X/nmUtKmBUxpib07T5y9oDIjgH1mX6ltjEqiVQNxdrG2ieaD0skqSIB+GoXnPpTOO3WOu9EEq3HjZ55dlD3doweUsiExxayt6wq5j6zP9pEya4DDXk3aanmWBUOKYlEyssOUFEVdDP3C3W2/Zw5sCvH9mwf7vYbuphXVimDerRj5ZY94Qto17Yt+LBkd82DCDHXMglN6rho3c7wMQSqdU8OdTWuVkUWZ8biSKG7osgpZSLPHT3Isa7qqdq2OdT5xKwtI3NZIkm2XRtAsqBdDxj7lGtgr0P0FO2hsRoh0X/4t58ziF+8uCzcHfeMo7vSsXUee8v2xzx+U0wiyVYW0dUrXi8ycIkGCF/wVn6xp9pAysHd2zG4e7vwv9mbK7fGPI4qNdYyCYm+CK/8Yk84rqC6rtmRMxwLMKaoJ+Aa8kOTaEZf/Kcs3BAei1Nj2v0YEqmeirdNou0T6dKeYurHEkkyfV7slsTtNAAmzk4oiUD1P8Sgujp5oMbstqE//OWbdlPlXVWCSrXup6bh6mp7CX3mb6zYwrgTe9UYAPn8+yV8uHEXFVVuNP9xPdvHPE5u1JT+0d/GIy/CC9bsqFaV9/A7axh5dFeyA27G4qysAFv3lIXHDYW2i76wh6bZD3l12ea4iSSR6qlkVGFZe0pms0SSLJ+8AjOuhjada4xUr0v0lOdVQQ3PFRV9UXvs32vo27lNEgM39RVU9w0/elGfyHEo5VUas7rtiC5tuGpE33AvrsgxJRcPLeSiiDEn4P6PZAUODpSsUpjz8RZysgOMPLoLb63cWqO7t1Bz/q+zB3erNmNzaNr92iRSPXWoVVixWHtKZrNxJA2lCgsegNd+Bj2GwPhp0KZLwruHvomu2rKHlz7cVGe1CjTeGAqTHN8a2JW5n2wN30XCwYb20AX4j6+vDP/bR74WeVGesnBDjfnNsgROPqJTuGE+tH9OljDG6w0WfWGP10biJ2sjSW82jiSVKg/A+0+7mXsvfAhyWyW865SFG/h51Cy1ibAkkjlysoRrTz2c047sUi0JRPayCn0bD6/dQuwxJJee1IsjD8vnwbc/41+fbEW9mYwHdWvL/M92oKoEBEYe3ZVrT619cstY0+6nAxvvkblsqd36KtsLFV9BTkuYMBvGPHVISaR4fSm31SOJmMwRagAf2ruAS0/qxfRrhzP+pF41lj0OVQ1delIvcr3BiaExJJc9sqDGkrrvrNrmkkZAmDi8D0/MXxdOUEGFeau2Jf292PK+Jh67I6mPLzfDlLHQ8QgY8zi0rnuFv0jF60u58+XlyViO3fgkyxtkWFUVDF/AQ0IzKYfWsw8JfeO+yBt8GKthffSQwhpjSCIbnqNnHl6++ctqMydH3ukk69u99agydcn4RCIio4C/AlnAI6p6V0pPuGU5TB4LX5WyavBNvO5NjAcwa0kJ0xdtoDJIeFK/AV3zyc/LDq9vXryhlMXrSy2JZKge7VswsHs7rjv1cMBd2KPXjZ/09X61rk8C8atwYo0hiWx4jm6UPntwNxat2xkeCR9vga36sh5Vpi4ZnUhEJAu4DzgTKAEWichLqvpxss/V55ZX+HrgI+7P+St5rduy+uzpjH5+L2UVKwmIm2ivImpG20XrSqv14Ile39w0vm/078SGnfs5rmd7+nfNZ1i/jtz96greW1d3lU0AuPSk3uG14uHg7Mmx1o2vr3i9oGK9duRh+XFXeGwo61Fl6pLRvbZEZDhwh6qe5T2/FUBVf1vbPvXptdXnlldoQRnv5N3Edm3Hd8v/h9NPOj48e6vxTwD4WmE7urRtwdwVW2ptczqhTwG3nH10zAts8fpSLvn7u+Hp5wXo2jaPYf060iovmxnFJeGVAptrtY71qDJNuddWD2BjxPMS4KTojURkEjAJoFev+n1bPEAeE8p/ygbtwl5aIVCtX79Jrvy8LA5UBr0ZcaFNXjZHds2nfatcdu0vZ+e+cvp2bsN1Xu+k4vWlvLNqm1v9MSvAaQM6o0CX/LyYXWAjDe1dwPRrT2bmkpIa64YAMds0mhvrUWXiyfREEmvUX40ru6o+BDwE7o6kvif7WPuEH48eUsig7u3CM8NmB1y//fy8bB56Z02tM8VmctoJ9TSKXFu9TW4W5VVBOrTK5Zie7cPra2zcuZ812/cRVKV7u5Z0bZtHWWWQvp1as3b7PvKyA7RvlQtAp/w8Bndvx1srt7LlywNccsKhd09t6KC4utot7CJqTO0yPZGUAD0jnhcCm5J9knV3fYc+t7wSfj7z+pPDF5dQ/XTkxevMQYcxa0kJ2/aUUbq/nPLKIJec0KtaXXboorm/rJI12/eRkxVg1ODD2Fdexdrt+yholcOArvmMHlLInOVf8OzijbTKzeKcY7qT3zKHgla5LN/kZpgN9QyKXKNk5pIStu8po1N+XniUdLzqiXSoukhG24Jd8I1pfJneRpINfAqMBD4HFgGXqury2vbxbYVEY4zJYE22jURVK0XkB8BruO6/j8VLIsYYY5IvoxMJgKr+A/iH33EYY0xzZVOkGGOMaRBLJMYYYxrEEokxxpgGsURijDGmQTK6+299iMg2YH09d+8EZOKEWRZ347K4G08mxgyZGXdvVe0c64Vml0gaQkQW19aPOp1Z3I3L4m48mRgzZG7ctbGqLWOMMQ1iicQYY0yDWCI5NA/5HUA9WdyNy+JuPJkYM2Ru3DFZG4kxxpgGsTsSY4wxDWKJxBhjTINYIkmQiIwSkZUislpEbvE5lp4i8qaIrBCR5SJyo1feQUTmiMgq73dBxD63erGvFJGzIsqHishS77V7RCTWYmHJjj9LRN4XkdmZEreItBeRGSLyife5D0/3uEXkZu//xzIRmSoiLdI1ZhF5TES2isiyiLKkxSoieSIy3StfKCJ9UhTz773/Ix+JyPMi0j6dYk4ZVbWfOn5wU9R/BvQDcoEPgYE+xtMNGOI9zsetyTIQ+B1wi1d+C3C393igF3Me0Nd7L1nea+8Bw3ELG74KnN0I8f8XMAWY7T1P+7iBJ4FrvMe5QPt0jhu3DPVaoKX3/FlgYrrGDHwDGAIsiyhLWqzA94EHvcfjgOkpivlbQLb3+O50izllfx9+B5AJP94/8msRz28FbvU7roh4XgTOBFYC3byybsDKWPHi1m8Z7m3zSUT5eODvKY61EJgLnM7BRJLWcQNtcRdliSpP27hxiWQj0AG3XMRs7yKXzjH3ibooJy3W0Dbe42zcqHJJdsxRr10ITE63mFPxY1VbiQn9UYaUeGW+8253jwcWAl1VdTOA97uLt1lt8ffwHkeXp9JfgJ8AwYiydI+7H7ANeNyrkntERFqnc9yq+jnwB2ADsBnYraqvp3PMMSQz1vA+qloJ7AY6pixy5yrcHUa180fFlm4x14slksTEqhP2vd+0iLQBZgI3qeqX8TaNUaZxylNCRM4BtqpqcaK7xChr9Lhx3waHAA+o6vHAPlxVS218j9trTzgfV43SHWgtIpfH26WW2NLx/359Ym3s/+u3AZXA5DrOnzYxN4QlksSUAD0jnhcCm3yKBQARycElkcmqOssr3iIi3bzXuwFbvfLa4i/xHkeXp8oI4DwRWQdMA04XkWcyIO4SoERVF3rPZ+ASSzrHfQawVlW3qWoFMAs4Oc1jjpbMWMP7iEg20A7YmYqgRWQCcA5wmXr1Uukec0NZIknMIqC/iPQVkVxcw9dLfgXj9ep4FFihqn+KeOklYIL3eAKu7SRUPs7rBdIX6A+851UX7BGRYd4xr4zYJ+lU9VZVLVTVPrjP8F+qenkGxP0FsFFEjvSKRgIfp3ncG4BhItLKO9dIYEWaxxwtmbFGHuti3P+9pH+7F5FRwE+B81R1f9R7ScuYk8LvRppM+QG+jesd9Rlwm8+xnIK7xf0I+MD7+Tau/nQusMr73SFin9u82FcS0esGKAKWea/dSyM15gGncbCxPe3jBo4DFnuf+QtAQbrHDfwS+MQ739O4HkNpGTMwFdeWU4H7Jn51MmMFWgDPAatxvaT6pSjm1bh2jdDf5YPpFHOqfmyKFGOMMQ1iVVvGGGMaxBKJMcaYBrFEYowxpkEskRhjjGkQSyTGGGMaxBKJSQviZtf9fhKPd5qInByjvI+IlIhIIKr8AxE5sZZjHSci305SXBeKiIrIUck4nnfMO0Tkc+89LBOR85J1bO/4p4k3U3Ocbap9RiJynvg8S7ZpPJZITLpoj5vttAYRyarH8U7DjeSuRlXX4fr5fz3i+EcB+ar6Xi3HOg43Tidh3kjkWMYD/8YNyEymP6vqccAY4LHoRNkIjiPiM1LVl1T1rkaOwfjEEolJF3cBh3vfqn/vfQt+U0SmAEsBROQFESkWt8bGpNCO4taKWSIiH4rIXG8iy+uAm73jfT3qXFOpfiEfB4TW63jcWxvifRH5pjeTwZ3AJd6xLhGR1uLWoljkbXe+F8dEEXlORF4GXo9+g97caCNwA9fGRZQHROR+733NFpF/iMjF3mtDReRt732/FpoypDaqugI3x1MnERnvvZdlInJ3xPn2isgfvc9sroh09srfEpEi73EncVPZRL+HE0XkXe99vysiR9byGU0UkXu9fXp75/nI+93LK39C3Pob74rImtB7NhnI7xGR9mM/qjGnED8NNzli34iyDt7vlriRwB2Bzrg7jL5R29wB/Hct5zoMNyI5tG7ECmAw8GPgca/sKNw0Iy1w63jcG7H/b4DLvcftcTMetPa2KyFiBHbUeS8HHvUev8vBNWUuBv6B+2J3GFDqleV423X2trsEeCzGccPvFTgJN1dTDy/+zrhJJ/8FXOBto7h5oABuD7034C2gyHvcCVgX8W8RmoWgbcTndgYw03sc/RlNjDjuy8AE7/FVwAve4ydwI7cDuPU6Vvv9/9B+6vdT2+23MengPVVdG/H8RyJyofe4J26+os7AvNB2qlrnpHaq+oWILAdGisgWoEJVl4nIr4C/edt8IiLrgQExDvEt3OST/+09bwH08h7PiRPDeNw0+uAmrRwPLMFNefOcqgaBL0TkTW+bI3EJbo6bhoksXAKM5WZxs/vuwSWcIuAtVd0GICKTcQsxvYCbwn+6t98zuAkdE9UOeFJE+uMSUk4C+wwHRnuPn8YtWBXygve+PxaRrocQh0kjlkhMOtsXeiAip+G+AQ9X1f0i8hbuAi7Ub2rtUPXWFu8xxJ62OxYBLlLVldUKRU6KjDnqtY64xbwGi4jikoKKyE/inFeA5ao6PIGY/qyqf4g43wUJ7BMS+vwqOVjd3aKWbX8FvKmqF3pViG8dwnmizwdQFvE45cs8m9SwNhKTLvbglg2uTTug1EsiRwHDvPL5wKniZlRFRDokeLyZuMbhS3B3BwDzgMu84wzA3WWsjHGs14AfioTX1j4+gfd3MfCUqvZW1T6q2hO36uIpuMb3i7y2kq64qiS8c3cWkeHeeXJEZFAC5wK30NmpXltHFu7u523vtYAXD8Cl3vkB1gFDI+KNpR3wufd4YkR5vM/7XQ62CV0WcT7TRFgiMWlBVXcA//Eahn8fY5N/Atki8hHuW/ECb79twCRgloh8yMEqm5eBC2tpbEdVd3nH2BJRfXY/kCUiS73jTFTVMuBNYGCoIdk7fw7wkYgs857XZTzwfFTZTNyFfCaubWUZ8HdcEtitquW4C/rd3nv7gBg90WJRNz35rV7sHwJLVDU0Pfk+YJCIFOPuku70yv8AXC8i7+LaSGL5HfBbEfkP7q4qJPozivQj4Lvev90VwI2JvAeTOWz2X2PSgIi0UdW9XhXYe8AIdeugpOJce1W1TSqObZonayMxJj3MFpH2QC7wq1QlEWNSwe5IjDHGNIi1kRhjjGkQSyTGGGMaxBKJMcaYBrFEYowxpkEskRhjjGmQ/wdB2NEB5900dgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SKIP THIS FOR NC V.2 **** What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP NC\n",
      "total state pop= 10439388 , VAP pct Black is  0.2009838261926679\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SKIP THIS FOR NC V.2 **** What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "245 4272 0.01240012275299989 nonPolygon tract no, pop, area\n",
      "3.414691650000176e-05\n",
      "2.6396099999900555e-07\n",
      "5.143744999998769e-07\n",
      "1.6841599999901352e-07\n",
      "9.20251499998282e-07\n",
      "0.012364108833499891\n",
      "1554 2305 0.09079886094049996 nonPolygon tract no, pop, area\n",
      "0.004141476349500224\n",
      "0.08665738459099974\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now I will print the list of interior lake Tracts\n",
      "[1, 47, 194, 491, 725, 726, 809, 1379, 1401, 1402, 1825, 1994, 2108, 2258]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "isSkippedTract = [0]*nTracts\n",
    "lakeTracts = [9999]\n",
    "minTractPop = 20\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        x2,y2 = tractGeom[m].exterior.xy  #turn these two on to show state map\n",
    "        if y < 999 and x > -999 : #optional to exclude some areas from plotting\n",
    "            plt.plot(x2,y2,c=\"purple\")\n",
    "            plt.text(x, y+0.00,m, fontsize=9)\n",
    "        #plt.scatter(x,y)\n",
    "        if x > -78.2:  #NC outer banks\n",
    "            if lakeTracts == [9999]:\n",
    "                lakeTracts = [m]\n",
    "                isSkippedTract[m] = 1\n",
    "            else:\n",
    "                if x > -78.2:  #m==99940 or m==999909:\n",
    "                    lakeTracts.append(m)\n",
    "                    isSkippedTract[m] = 1\n",
    "                else:\n",
    "                    thisTract = \"interior\"\n",
    "                    \n",
    "print(\"Now I will print the list of interior lake Tracts\")\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2672 14\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PREC_ID</th>\n",
       "      <th>ENR_DESC</th>\n",
       "      <th>COUNTY_NAM</th>\n",
       "      <th>COUNTY_ID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRECBLA</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>...</th>\n",
       "      <th>G20SACDSHI</th>\n",
       "      <th>G20SACRGOR</th>\n",
       "      <th>G20SACDCUB</th>\n",
       "      <th>G20SACRDIL</th>\n",
       "      <th>G20SACDSTY</th>\n",
       "      <th>G20SACRCAR</th>\n",
       "      <th>G20SACDYOU</th>\n",
       "      <th>G20SACRGRI</th>\n",
       "      <th>G20SACDBRO</th>\n",
       "      <th>geometry</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>PATTERSON</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>1</td>\n",
       "      <td>2299</td>\n",
       "      <td>566</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>566</td>\n",
       "      <td>2276</td>\n",
       "      <td>571</td>\n",
       "      <td>2296</td>\n",
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       "      <td>2280</td>\n",
       "      <td>567</td>\n",
       "      <td>2274</td>\n",
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       "      <td>POLYGON ((1839239.963 762333.301, 1839240.297 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>COBLE</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>1</td>\n",
       "      <td>2387</td>\n",
       "      <td>559</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "      <td>558</td>\n",
       "      <td>2347</td>\n",
       "      <td>562</td>\n",
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       "      <td>2349</td>\n",
       "      <td>555</td>\n",
       "      <td>2359</td>\n",
       "      <td>543</td>\n",
       "      <td>POLYGON ((1840088.847 807206.254, 1840090.437 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07</td>\n",
       "      <td>ALBRIGHT</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>1</td>\n",
       "      <td>1996</td>\n",
       "      <td>581</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>578</td>\n",
       "      <td>1945</td>\n",
       "      <td>596</td>\n",
       "      <td>1977</td>\n",
       "      <td>570</td>\n",
       "      <td>1963</td>\n",
       "      <td>579</td>\n",
       "      <td>1954</td>\n",
       "      <td>585</td>\n",
       "      <td>POLYGON ((1871943.040 801230.531, 1871943.510 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H08</td>\n",
       "      <td>H08</td>\n",
       "      <td>GUILFORD</td>\n",
       "      <td>41</td>\n",
       "      <td>73</td>\n",
       "      <td>614</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>607</td>\n",
       "      <td>76</td>\n",
       "      <td>612</td>\n",
       "      <td>74</td>\n",
       "      <td>614</td>\n",
       "      <td>77</td>\n",
       "      <td>602</td>\n",
       "      <td>76</td>\n",
       "      <td>602</td>\n",
       "      <td>POLYGON ((1702354.926 805008.445, 1702627.359 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>079</td>\n",
       "      <td>079</td>\n",
       "      <td>MECKLENBURG</td>\n",
       "      <td>60</td>\n",
       "      <td>469</td>\n",
       "      <td>1223</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1212</td>\n",
       "      <td>452</td>\n",
       "      <td>1227</td>\n",
       "      <td>465</td>\n",
       "      <td>1209</td>\n",
       "      <td>454</td>\n",
       "      <td>1224</td>\n",
       "      <td>456</td>\n",
       "      <td>1220</td>\n",
       "      <td>POLYGON ((1410451.445 548338.307, 1410462.273 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PREC_ID   ENR_DESC   COUNTY_NAM  COUNTY_ID  G20PRERTRU  G20PREDBID  \\\n",
       "0      01  PATTERSON     ALAMANCE          1        2299         566   \n",
       "1      02      COBLE     ALAMANCE          1        2387         559   \n",
       "2      07   ALBRIGHT     ALAMANCE          1        1996         581   \n",
       "3     H08        H08     GUILFORD         41          73         614   \n",
       "4     079        079  MECKLENBURG         60         469        1223   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PRECBLA  G20PREOWRI  ...  G20SACDSHI  \\\n",
       "0          27           6           5           4  ...         566   \n",
       "1          15           4           4           3  ...         558   \n",
       "2          20           9           1           4  ...         578   \n",
       "3           3           1           2           2  ...         607   \n",
       "4           8           9           2           6  ...        1212   \n",
       "\n",
       "   G20SACRGOR  G20SACDCUB  G20SACRDIL  G20SACDSTY  G20SACRCAR  G20SACDYOU  \\\n",
       "0        2276         571        2296         548        2280         567   \n",
       "1        2347         562        2367         534        2349         555   \n",
       "2        1945         596        1977         570        1963         579   \n",
       "3          76         612          74         614          77         602   \n",
       "4         452        1227         465        1209         454        1224   \n",
       "\n",
       "   G20SACRGRI  G20SACDBRO                                           geometry  \n",
       "0        2274         568  POLYGON ((1839239.963 762333.301, 1839240.297 ...  \n",
       "1        2359         543  POLYGON ((1840088.847 807206.254, 1840090.437 ...  \n",
       "2        1954         585  POLYGON ((1871943.040 801230.531, 1871943.510 ...  \n",
       "3          76         602  POLYGON ((1702354.926 805008.445, 1702627.359 ...  \n",
       "4         456        1220  POLYGON ((1410451.445 548338.307, 1410462.273 ...  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/nc_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "e61d9528-9aba-4b7e-a156-b595b44f474c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PREC_ID</th>\n",
       "      <th>ENR_DESC</th>\n",
       "      <th>COUNTY_NAM</th>\n",
       "      <th>COUNTY_ID</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRECBLA</th>\n",
       "      <th>G20PREOWRI</th>\n",
       "      <th>...</th>\n",
       "      <th>G20SACDSHI</th>\n",
       "      <th>G20SACRGOR</th>\n",
       "      <th>G20SACDCUB</th>\n",
       "      <th>G20SACRDIL</th>\n",
       "      <th>G20SACDSTY</th>\n",
       "      <th>G20SACRCAR</th>\n",
       "      <th>G20SACDYOU</th>\n",
       "      <th>G20SACRGRI</th>\n",
       "      <th>G20SACDBRO</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>PATTERSON</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>1</td>\n",
       "      <td>2299</td>\n",
       "      <td>566</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>566</td>\n",
       "      <td>2276</td>\n",
       "      <td>571</td>\n",
       "      <td>2296</td>\n",
       "      <td>548</td>\n",
       "      <td>2280</td>\n",
       "      <td>567</td>\n",
       "      <td>2274</td>\n",
       "      <td>568</td>\n",
       "      <td>POLYGON ((-79.54243 35.84340, -79.54242 35.843...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>COBLE</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>1</td>\n",
       "      <td>2387</td>\n",
       "      <td>559</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>558</td>\n",
       "      <td>2347</td>\n",
       "      <td>562</td>\n",
       "      <td>2367</td>\n",
       "      <td>534</td>\n",
       "      <td>2349</td>\n",
       "      <td>555</td>\n",
       "      <td>2359</td>\n",
       "      <td>543</td>\n",
       "      <td>POLYGON ((-79.54039 35.96668, -79.54038 35.966...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>07</td>\n",
       "      <td>ALBRIGHT</td>\n",
       "      <td>ALAMANCE</td>\n",
       "      <td>1</td>\n",
       "      <td>1996</td>\n",
       "      <td>581</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>578</td>\n",
       "      <td>1945</td>\n",
       "      <td>596</td>\n",
       "      <td>1977</td>\n",
       "      <td>570</td>\n",
       "      <td>1963</td>\n",
       "      <td>579</td>\n",
       "      <td>1954</td>\n",
       "      <td>585</td>\n",
       "      <td>POLYGON ((-79.43265 35.95070, -79.43265 35.950...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>H08</td>\n",
       "      <td>H08</td>\n",
       "      <td>GUILFORD</td>\n",
       "      <td>41</td>\n",
       "      <td>73</td>\n",
       "      <td>614</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>607</td>\n",
       "      <td>76</td>\n",
       "      <td>612</td>\n",
       "      <td>74</td>\n",
       "      <td>614</td>\n",
       "      <td>77</td>\n",
       "      <td>602</td>\n",
       "      <td>76</td>\n",
       "      <td>602</td>\n",
       "      <td>POLYGON ((-80.00573 35.95770, -80.00481 35.958...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>079</td>\n",
       "      <td>079</td>\n",
       "      <td>MECKLENBURG</td>\n",
       "      <td>60</td>\n",
       "      <td>469</td>\n",
       "      <td>1223</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1212</td>\n",
       "      <td>452</td>\n",
       "      <td>1227</td>\n",
       "      <td>465</td>\n",
       "      <td>1209</td>\n",
       "      <td>454</td>\n",
       "      <td>1224</td>\n",
       "      <td>456</td>\n",
       "      <td>1220</td>\n",
       "      <td>POLYGON ((-80.97461 35.24055, -80.97459 35.241...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PREC_ID   ENR_DESC   COUNTY_NAM  COUNTY_ID  G20PRERTRU  G20PREDBID  \\\n",
       "0      01  PATTERSON     ALAMANCE          1        2299         566   \n",
       "1      02      COBLE     ALAMANCE          1        2387         559   \n",
       "2      07   ALBRIGHT     ALAMANCE          1        1996         581   \n",
       "3     H08        H08     GUILFORD         41          73         614   \n",
       "4     079        079  MECKLENBURG         60         469        1223   \n",
       "\n",
       "   G20PRELJOR  G20PREGHAW  G20PRECBLA  G20PREOWRI  ...  G20SACDSHI  \\\n",
       "0          27           6           5           4  ...         566   \n",
       "1          15           4           4           3  ...         558   \n",
       "2          20           9           1           4  ...         578   \n",
       "3           3           1           2           2  ...         607   \n",
       "4           8           9           2           6  ...        1212   \n",
       "\n",
       "   G20SACRGOR  G20SACDCUB  G20SACRDIL  G20SACDSTY  G20SACRCAR  G20SACDYOU  \\\n",
       "0        2276         571        2296         548        2280         567   \n",
       "1        2347         562        2367         534        2349         555   \n",
       "2        1945         596        1977         570        1963         579   \n",
       "3          76         612          74         614          77         602   \n",
       "4         452        1227         465        1209         454        1224   \n",
       "\n",
       "   G20SACRGRI  G20SACDBRO                                           geometry  \n",
       "0        2274         568  POLYGON ((-79.54243 35.84340, -79.54242 35.843...  \n",
       "1        2359         543  POLYGON ((-79.54039 35.96668, -79.54038 35.966...  \n",
       "2        1954         585  POLYGON ((-79.43265 35.95070, -79.43265 35.950...  \n",
       "3          76         602  POLYGON ((-80.00573 35.95770, -80.00481 35.958...  \n",
       "4         456        1220  POLYGON ((-80.97461 35.24055, -80.97459 35.241...  \n",
       "\n",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #NC's precinct file in wrong CRS\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the highest county number I found is 100\n",
      "2662 0.5068420065378582 5443067 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays  #*** ... THAT WE WILL LATER DIVIDE INTO SMALLER PIECES BY BLOCK-GROUP INTERSXNS\n",
    "bigVTDGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "bigVTDTrump = VTDdbf['G20PRERTRU']   #we will later convert to vtdTrump and vtdBiden after overlaying census data\n",
    "bigVTDBiden = VTDdbf['G20PREDBID']\n",
    "bigVTDcounty = VTDdbf['COUNTY_ID']\n",
    "print(\"the highest county number I found is\",np.max(bigVTDcounty))\n",
    "\n",
    "nPrecincts = len(bigVTDGeom)\n",
    "stateGOP = np.sum(bigVTDTrump)/(np.sum(bigVTDTrump) + np.sum(bigVTDBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(bigVTDTrump) + np.sum(bigVTDBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "#vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "#vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "95b52c46-037a-41db-bc09-52bde0423598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have read in  7111 rows of block group pop data\n"
     ]
    }
   ],
   "source": [
    "# Now, read in a block-group file as the basis for carving up the precincts by population\n",
    "block_dbf = gpd.read_file(\"state_map_files/nc_pl2020_bg.dbf\")\n",
    "block_dbf.head()\n",
    "blockGeom = block_dbf['geometry']\n",
    "blockVAP = block_dbf['P0030001']\n",
    "nBlockGroups = len(blockVAP)\n",
    "print(\"I have read in \",nBlockGroups,\"rows of block group pop data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "18250632-9b27-479c-b1db-c0c70343cab3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now convert Census block-group county IDs to NC county numbers, numbering 1 to max\n",
      "the min and max county numbers I found were 1 and 100\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now convert Census block-group county IDs to NC county numbers, numbering 1 to max\")\n",
    "doublebgCountyNo = block_dbf['COUNTYFP20']\n",
    "bgCountyNo = [-999]*nBlockGroups\n",
    "for t in range(nBlockGroups):\n",
    "    bgCountyNo[t] = int( (int(doublebgCountyNo[t])+1)/2 )  #Census captures county numbers as odds, skipping evens\n",
    "print(\"the min and max county numbers I found were\",np.min(bgCountyNo),\"and\",np.max(bgCountyNo) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "9898b687-327a-4b58-bcb9-739e75c2a7e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on precinct number 0\n",
      "working on precinct number 100\n",
      "working on precinct number 200\n",
      "working on precinct number 300\n",
      "working on precinct number 400\n",
      "working on precinct number 500\n",
      "working on precinct number 600\n",
      "working on precinct number 700\n",
      "working on precinct number 800\n",
      "working on precinct number 900\n",
      "working on precinct number 1000\n",
      "working on precinct number 1100\n",
      "working on precinct number 1200\n",
      "working on precinct number 1300\n",
      "working on precinct number 1400\n",
      "working on precinct number 1500\n",
      "working on precinct number 1600\n",
      "working on precinct number 1700\n",
      "working on precinct number 1800\n",
      "working on precinct number 1900\n",
      "working on precinct number 2000\n",
      "working on precinct number 2100\n",
      "working on precinct number 2200\n",
      "working on precinct number 2300\n",
      "working on precinct number 2400\n",
      "working on precinct number 2500\n",
      "working on precinct number 2600\n",
      "all done dividing 2662 precincts into  12723  =  12723 precinct x block groups\n",
      "total VAP is now 8152715.912866237 was originally 8155099\n",
      "red-blue lean is now 0.5068420065378582 was originally 0.5068420065378582\n"
     ]
    }
   ],
   "source": [
    "#DIVIDE UP THE PRECINCTS INTO THEIR BLOCK SUBGROUPS #about 3min to run for NC, using county restriction\n",
    "nPrecincts = len(bigVTDGeom)\n",
    "dummyPoly = Polygon([(0,0),(1,0),(1,1)])\n",
    "vtdGeom = [dummyPoly]*1  #we don't know a priori how many precinct x blockgroups there will be\n",
    "vtdTrump = [-99999.]*1   # ... so we start arrays for these variables, and append as we go\n",
    "vtdBiden = [-99999.]*1\n",
    "vtdVAP = [-99999.]*1  #total voting-age pop in the overlap of a block group and a precinct\n",
    "vtdPrecinctNo = [-99999]*1\n",
    "bigVTDnParts = [0]*nPrecincts  #this tracks how many block groups have at least some intersxn w each true precinct\n",
    "totalCounter = 0\n",
    "minAreaRatio = 0.001 #fraction of precinct area for a precinct-blockgroup intersection to \"count\"\n",
    "for p in range(nPrecincts):\n",
    "    minArea = minAreaRatio*bigVTDGeom[p].area  #we only bother with intersections that are at least 0.1% of the precinct's area\n",
    "    pC = int(bigVTDcounty[p])  #shorthand for the precinct's county\n",
    "    if p%100 == 0:\n",
    "        print(\"working on precinct number\",p)\n",
    "    precinctVAP = 0.\n",
    "    overlapCounter = 0  #reset how many block-group overlaps for this precinct\n",
    "    for bg in range(nBlockGroups):\n",
    "        bgC = int(bgCountyNo[bg])\n",
    "        if pC == bgC or (bgC > 54 and bgC < 60 and pC > 54 and pC < 60) :  #restrict search to overlaps in same county\n",
    "            # counties 56 - 59 are shuffled in NC vtd file, so the above \"or\" finds them\n",
    "            overlapPoly = blockGeom[bg].intersection(bigVTDGeom[p])\n",
    "            if overlapPoly.area > minArea:   #we have sufficient overlap of this precinct and block group\n",
    "                # first, check if this intersxn is a simple polygon.  If not, we eliminate any Points or Linestrings\n",
    "                if type(overlapPoly) != type(tractGeom[0]) :\n",
    "                    polly = dummyPoly\n",
    "                    for geom in overlapPoly.geoms:\n",
    "                        if type(geom) == type(tractGeom[0]) :  #this sub-geom is a polygon, not a point or LineString, so we include it\n",
    "                            if polly == dummyPoly:  #is this the first polygon in the intersection?\n",
    "                                polly = geom\n",
    "                            else:\n",
    "                                polly = polly.union(geom)\n",
    "                    overlapPoly = polly\n",
    "                bigVTDnParts[p] +=1   #this precinct now has another block associated with it\n",
    "                fracArea = overlapPoly.area/blockGeom[bg].area\n",
    "                overlapVAP = fracArea*blockVAP[bg]\n",
    "                precinctVAP += overlapVAP   #running total of VAP contributions from intersecting block groups\n",
    "                if totalCounter == 0:\n",
    "                    vtdGeom[0] = overlapPoly\n",
    "                    vtdVAP[0] = overlapVAP\n",
    "                    vtdPrecinctNo[0] = p   #which precinct no does this new sub-precinct belong to?\n",
    "                else:\n",
    "                    vtdGeom.append(overlapPoly)\n",
    "                    vtdTrump.append(0.)  #(fracArea * bigVTDTrump[p] )  #we assign these later after we know total VAP\n",
    "                    vtdBiden.append(0.)  #(fracArea * bigVTDBiden[p] )\n",
    "                    vtdVAP.append(overlapVAP)   \n",
    "                    vtdPrecinctNo.append(p)\n",
    "                overlapCounter +=1\n",
    "                totalCounter +=1\n",
    "    # now assign sub-precinct Trump and Biden votes based on their fraction of this precinct's total VAP\n",
    "    ratio = 0.\n",
    "    \n",
    "    #for v in range(totalCounter-overlapCounter,totalCounter,1): #debug\n",
    "        #ratio += vtdVAP[v]/precinctVAP\n",
    "    #if ratio < 0.98 or ratio > 1.02 :\n",
    "        #print(p,\"precinct VAP was\",precinctVAP,\"but total assigned vtdVAP to precinct was\",round(ratio*precinctVAP,3) )\n",
    "        \n",
    "    for v in range(totalCounter-overlapCounter,totalCounter,1):\n",
    "        vtdTrump[v] = vtdVAP[v]/precinctVAP * bigVTDTrump[p]\n",
    "        vtdBiden[v] = vtdVAP[v]/precinctVAP * bigVTDBiden[p]\n",
    "            \n",
    "nBigPrecincts = nPrecincts   #this is the true number of precincts\n",
    "nPrecincts = len(vtdGeom)   #to be consistent with nomenclature in subsequent code; these are subprecincts\n",
    "oldStateGOP = np.sum(bigVTDTrump)/(np.sum(bigVTDTrump) + np.sum(bigVTDBiden) ) \n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump)+np.sum(vtdBiden))\n",
    "print(\"all done dividing\",nBigPrecincts,\"precincts into \",nPrecincts,\" = \",totalCounter,\"precinct x block groups\")\n",
    "print(\"total VAP is now\",np.sum(vtdVAP),\"was originally\",np.sum(blockVAP) )\n",
    "print(\"red-blue lean is now\",stateGOP,\"was originally\",oldStateGOP )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "01de17c3-8530-4254-8e8c-b888d5ed3608",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the distro of precinct x block group Trump votes by Biden votes\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Here is the distro of precinct x block group Trump votes by Biden votes\")\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct-blockgroup overlap\", ylabel=\"Trump precint+bg votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "036f4a37-1f16-4f2d-a459-3bd9395068f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80 unoccupied precinct found at -78.88138275653672 36.124195653537804\n",
      "280 unoccupied precinct found at -79.94758274816343 35.32017561917642\n",
      "297 tiny precinct found at -79.41328789741237 36.07490111151044 with area= 6.5e-07 and voters 3.93987\n",
      "299 tiny precinct found at -79.41560663894491 36.08502349691819 with area= 4.6e-07 and voters 2.27088\n",
      "300 tiny precinct found at -79.41354694141646 36.07222215372957 with area= 8.8e-07 and voters 4.83313\n",
      "362 tiny precinct found at -79.41349376645549 36.08004563629671 with area= 6.8e-07 and voters 2.73135\n",
      "363 tiny precinct found at -79.41112764990372 36.0835109769934 with area= 5.2e-07 and voters 1.95702\n",
      "400 unoccupied precinct found at -83.51447070368188 35.511911852303356\n",
      "464 unoccupied precinct found at -77.96611176789804 33.865789310153836\n",
      "686 tiny precinct found at -77.93186183833012 35.378846916327234 with area= 8.2e-07 and voters 1.48813\n",
      "715 tiny precinct found at -77.97999907999379 35.390288818197604 with area= 3e-07 and voters 1.70013\n",
      "794 tiny precinct found at -80.81750693728057 35.027573899358465 with area= 9.4e-07 and voters 9.12375\n",
      "797 tiny precinct found at -80.8295968811582 35.03868193333455 with area= 6.5e-07 and voters 4.81237\n",
      "803 unoccupied precinct found at -76.20339076481366 36.08234770425234\n",
      "1025 unoccupied precinct found at -77.66489975496646 34.301586236948424\n",
      "1046 unoccupied precinct found at -76.30742949293175 36.05735305854505\n",
      "1169 unoccupied precinct found at -76.49302580655863 35.13325637786162\n",
      "1402 tiny precinct found at -78.87001279145124 35.99320224896953 with area= 5.9e-07 and voters 1.516\n",
      "1437 tiny precinct found at -78.90601364708427 35.99091965706772 with area= 2.9e-07 and voters 2.64204\n",
      "1619 tiny precinct found at -80.56423637512164 34.986895931241705 with area= 7.1e-07 and voters 1.47262\n",
      "1768 unoccupied precinct found at -77.41321296096478 34.46531787553828\n",
      "1804 unoccupied precinct found at -77.14123847864232 34.60642160179901\n",
      "1847 unoccupied precinct found at -77.26554228456199 36.4857462707912\n",
      "2142 unoccupied precinct found at -78.49928004146565 33.8426127104882\n",
      "2220 tiny precinct found at -80.64255419647832 35.5322799542313 with area= 6.7e-07 and voters 0.44411\n",
      "2220 unoccupied precinct found at -80.64255419647832 35.5322799542313\n",
      "2226 unoccupied precinct found at -80.58137252023624 35.72815454961532\n",
      "2274 tiny precinct found at -80.78098810268578 35.338930905293076 with area= 8.2e-07 and voters 6.18897\n",
      "2303 tiny precinct found at -80.87405973687522 35.405799003107994 with area= 8.4e-07 and voters 5.79856\n",
      "2325 tiny precinct found at -80.76295267147117 35.313362344841366 with area= 7.7e-07 and voters 1.23688\n",
      "2485 unoccupied precinct found at -78.90174059139349 34.99499191741674\n",
      "2566 tiny precinct found at -82.58004899789859 35.57811351843458 with area= 2.8e-07 and voters 1.54255\n",
      "2675 tiny precinct found at -82.54521068334226 35.623387806339004 with area= 3.6e-07 and voters 0.87209\n",
      "2745 unoccupied precinct found at -80.96344467747251 35.215139959304445\n",
      "2888 unoccupied precinct found at -82.56782837492965 35.89247881478976\n",
      "2979 unoccupied precinct found at -82.69445412897247 35.4217483371155\n",
      "3072 unoccupied precinct found at -76.25632701213377 35.24699331508616\n",
      "3075 unoccupied precinct found at -75.90583241331595 35.50317092736611\n",
      "3114 tiny precinct found at -79.91363863256876 36.01823223701271 with area= 7.1e-07 and voters 1.68304\n",
      "3691 tiny precinct found at -80.30830971116652 36.08282339444177 with area= 3.7e-07 and voters 1.29462\n",
      "3713 unoccupied precinct found at -75.92283601562603 35.98539726112924\n",
      "3771 unoccupied precinct found at -77.77403606800831 35.9263018033584\n",
      "3816 unoccupied precinct found at -79.96016191934936 36.09939261823843\n",
      "3953 unoccupied precinct found at -79.94090672155508 36.11391493802772\n",
      "4057 unoccupied precinct found at -82.67425388571925 35.36541548306776\n",
      "4131 tiny precinct found at -80.22995034013218 36.12288243262603 with area= 1.1e-07 and voters 0.58352\n",
      "4131 unoccupied precinct found at -80.22995034013218 36.12288243262603\n",
      "4175 tiny precinct found at -80.24007748818096 36.080338771343825 with area= 9.3e-07 and voters 2.42749\n",
      "4176 tiny precinct found at -80.2406786594506 36.08114218036666 with area= 5.1e-07 and voters 1.66456\n",
      "4342 tiny precinct found at -80.35162052344437 36.11650495231843 with area= 6.1e-07 and voters 3.40431\n",
      "4364 unoccupied precinct found at -79.93425062330724 36.10228333296593\n",
      "4419 tiny precinct found at -80.34867312417607 36.120644437078646 with area= 7.1e-07 and voters 1.45186\n",
      "4672 tiny precinct found at -80.87758374888769 35.141528009786605 with area= 9.5e-07 and voters 3.3239\n",
      "4928 tiny precinct found at -80.87396604204902 35.19249552778871 with area= 1e-06 and voters 6.16576\n",
      "5002 unoccupied precinct found at -76.62270163580031 35.95673358931326\n",
      "5026 unoccupied precinct found at -78.88107808036821 36.13022126256183\n",
      "5048 unoccupied precinct found at -78.86844843157917 35.9432052221119\n",
      "5083 unoccupied precinct found at -80.94438061396284 35.21081042835599\n",
      "5385 unoccupied precinct found at -75.85317462848758 36.07746443214581\n",
      "5916 tiny precinct found at -78.88666813220212 35.98302713290722 with area= 9.1e-07 and voters 9.8871\n",
      "5963 unoccupied precinct found at -78.30446461384945 33.884052085049085\n",
      "5977 unoccupied precinct found at -78.04127454917707 33.845967749272816\n",
      "5985 unoccupied precinct found at -77.75291735164987 34.208752505867835\n",
      "6109 unoccupied precinct found at -80.95345192755825 35.09691117628245\n",
      "6141 unoccupied precinct found at -75.96339687696903 36.12933742584546\n",
      "6161 tiny precinct found at -78.67446108553492 35.837626506047066 with area= 5.8e-07 and voters 4.4096\n",
      "6308 unoccupied precinct found at -81.9052272113704 36.004502870494775\n",
      "6384 unoccupied precinct found at -75.45082468043177 35.66613319826703\n",
      "6385 unoccupied precinct found at -75.63177686273389 35.62494423687402\n",
      "6403 tiny precinct found at -78.7109804201374 35.86198341439489 with area= 4.6e-07 and voters 3.19591\n",
      "6422 unoccupied precinct found at -75.68215945468741 35.84010495642199\n",
      "6425 unoccupied precinct found at -75.62797202039684 35.786541716536696\n",
      "6452 unoccupied precinct found at -76.84637802654935 34.66356805313516\n",
      "6456 unoccupied precinct found at -76.17624500737101 34.92295294273487\n",
      "6457 unoccupied precinct found at -76.18569231704683 34.96526301415037\n",
      "6458 unoccupied precinct found at -76.23231685962368 35.09225925865122\n",
      "6501 unoccupied precinct found at -78.7633753675678 35.85498845614592\n",
      "6502 unoccupied precinct found at -78.7889195563882 35.872892875092234\n",
      "6691 tiny precinct found at -82.57183686469844 35.5900617414209 with area= 6e-07 and voters 4.10146\n",
      "6859 unoccupied precinct found at -76.66895241752266 35.98086370589564\n",
      "6984 tiny precinct found at -78.40475912446563 36.32538507871728 with area= 6.9e-07 and voters 1.96835\n",
      "7110 tiny precinct found at -80.49381526895854 35.688345015249034 with area= 7.4e-07 and voters 5.11924\n",
      "7690 unoccupied precinct found at -78.74271339516517 35.836280644569705\n",
      "7881 tiny precinct found at -78.658560172702 35.816417371031136 with area= 9.5e-07 and voters 4.50228\n",
      "7899 unoccupied precinct found at -78.74708169798666 35.86559481274138\n",
      "7900 unoccupied precinct found at -78.76527224310937 35.89065653540323\n",
      "7953 tiny precinct found at -78.66334183694852 35.81926933427812 with area= 7.6e-07 and voters 3.70027\n",
      "8035 tiny precinct found at -78.64619150884508 35.7986966247366 with area= 1.8e-07 and voters 1.26105\n",
      "8036 tiny precinct found at -78.64601621757856 35.80186640887991 with area= 4.9e-07 and voters 6.58543\n",
      "8215 unoccupied precinct found at -78.42522037724697 33.8626732714783\n",
      "8234 tiny precinct found at -78.61362061084469 35.77866308813211 with area= 2.6e-07 and voters 0.69119\n",
      "8401 unoccupied precinct found at -78.74922242648678 35.89152477933565\n",
      "8533 tiny precinct found at -78.57931525196429 35.79652913321191 with area= 7.9e-07 and voters 1.87001\n",
      "8534 unoccupied precinct found at -79.01609799666615 34.332236728602744\n",
      "8708 tiny precinct found at -78.55019564755452 35.96408655260281 with area= 4.2e-07 and voters 2.93378\n",
      "8728 unoccupied precinct found at -76.3647967020923 34.76427822387124\n",
      "8729 unoccupied precinct found at -76.40817345513325 34.78512932150805\n",
      "8730 unoccupied precinct found at -76.41240721432942 35.103142309331695\n",
      "8755 tiny precinct found at -78.72125946134793 35.77136814777213 with area= 7.2e-07 and voters 7.71972\n",
      "9252 tiny precinct found at -79.09678584981039 35.918636107097214 with area= 5.6e-07 and voters 2.25006\n",
      "9290 unoccupied precinct found at -76.09617515700612 36.10586151821622\n",
      "9388 tiny precinct found at -78.96926050496508 35.08515025747961 with area= 6.9e-07 and voters 1.57269\n",
      "9394 tiny precinct found at -79.0546921510958 35.92543583954491 with area= 8.1e-07 and voters 14.18483\n",
      "9398 unoccupied precinct found at -75.46047964714337 35.399592308061735\n",
      "9399 unoccupied precinct found at -75.65072693099876 35.4014653578505\n",
      "9438 unoccupied precinct found at -75.55708294210778 35.89665008507826\n",
      "9440 unoccupied precinct found at -75.67431560237092 35.96341392061011\n",
      "9527 tiny precinct found at -79.06840539040715 35.91694163058558 with area= 6.7e-07 and voters 4.28351\n",
      "9554 unoccupied precinct found at -77.85416425563645 34.044363331345835\n",
      "9778 unoccupied precinct found at -77.81133024083569 34.1294821836131\n",
      "9786 tiny precinct found at -77.91918673220863 34.23491244898517 with area= 4.7e-07 and voters 5.79161\n",
      "9812 unoccupied precinct found at -77.90529013769522 34.26984097677341\n",
      "9820 unoccupied precinct found at -77.89408032043455 33.93380191533905\n",
      "9828 unoccupied precinct found at -77.86368883703359 34.082445384619895\n",
      "9891 unoccupied precinct found at -83.03638380604978 35.14847493393002\n",
      "10229 unoccupied precinct found at -82.99153128440055 35.665183645058995\n",
      "10231 unoccupied precinct found at -83.04285073587742 35.498243768183904\n",
      "10404 tiny precinct found at -80.00034758436081 35.94253828617531 with area= 8.7e-07 and voters 2.23495\n",
      "10462 unoccupied precinct found at -80.82825167004049 35.8110553777436\n",
      "10478 tiny precinct found at -80.03575925963972 36.00373003602964 with area= 7.7e-07 and voters 1.89991\n",
      "10545 unoccupied precinct found at -75.97000892470048 35.31028373607105\n",
      "10554 unoccupied precinct found at -75.90555870035219 35.1712134595251\n",
      "10555 unoccupied precinct found at -75.88113795833372 35.106997172160824\n",
      "10557 unoccupied precinct found at -76.46007042553136 35.35196384910664\n",
      "10589 unoccupied precinct found at -82.41279746200254 35.24769519869185\n",
      "10640 tiny precinct found at -82.97460841267804 35.49285854987951 with area= 9.7e-07 and voters 0.83518\n",
      "10651 tiny precinct found at -83.00997520980758 35.479792817368285 with area= 2.9e-07 and voters 0.86733\n",
      "10701 tiny precinct found at -79.76661716986742 36.08454883975667 with area= 9.9e-07 and voters 1.73722\n",
      "10710 tiny precinct found at -79.77224398591342 36.04576300834522 with area= 9.5e-07 and voters 4.87994\n",
      "10729 tiny precinct found at -79.97980285167837 35.97977661283328 with area= 8.9e-07 and voters 1.47222\n",
      "10764 unoccupied precinct found at -79.71666847492178 36.10204808326092\n",
      "10825 tiny precinct found at -79.81744236292387 36.06271233517323 with area= 7.4e-07 and voters 9.07553\n",
      "10834 tiny precinct found at -79.84905882399455 36.03013741039187 with area= 4.5e-07 and voters 5.38789\n",
      "10870 unoccupied precinct found at -75.69337690607196 36.133084975759274\n",
      "10888 unoccupied precinct found at -75.81931516026147 36.48643711951994\n",
      "10968 unoccupied precinct found at -79.92654924948474 36.09505481295598\n",
      "11118 unoccupied precinct found at -79.62781656415208 35.968251677581236\n",
      "11256 tiny precinct found at -78.97701751699994 35.00218276458617 with area= 7.8e-07 and voters 4.02057\n",
      "11309 unoccupied precinct found at -75.62647915897786 35.26242595690503\n",
      "11321 unoccupied precinct found at -75.53704349199812 35.21436258617556\n",
      "11322 unoccupied precinct found at -75.56819521927287 35.28100388045598\n",
      "11323 unoccupied precinct found at -75.7806950293415 36.001999367713104\n",
      "11325 unoccupied precinct found at -75.79858553498072 36.027654758022585\n",
      "11326 unoccupied precinct found at -75.78255548799373 35.93700687618079\n",
      "11327 unoccupied precinct found at -75.68842323072107 35.77835740763605\n",
      "11328 unoccupied precinct found at -75.6931269456932 35.86712683436968\n",
      "11335 unoccupied precinct found at -75.72705947247624 35.92358811369525\n",
      "11337 unoccupied precinct found at -75.69255418996752 35.95403130362369\n",
      "11341 unoccupied precinct found at -75.73413955493785 35.99412653123558\n",
      "11342 unoccupied precinct found at -75.75270509256275 36.03017530223701\n",
      "11347 unoccupied precinct found at -75.6842034903164 35.182123647950434\n",
      "11348 unoccupied precinct found at -75.70638623057961 35.233706242928086\n",
      "11353 unoccupied precinct found at -75.6332004120094 36.032809916136664\n",
      "11356 unoccupied precinct found at -75.7226671856707 36.197493927418\n",
      "11361 unoccupied precinct found at -75.75537269199191 36.04491842436437\n",
      "11362 unoccupied precinct found at -75.66599662519242 36.08687412110246\n",
      "11381 unoccupied precinct found at -75.77363280424585 36.328765884284\n",
      "11585 tiny precinct found at -79.01117350740975 35.10309916139501 with area= 9.4e-07 and voters 0.02176\n",
      "11585 unoccupied precinct found at -79.01117350740975 35.10309916139501\n",
      "11600 tiny precinct found at -79.01598615299551 35.061463156094504 with area= 4.9e-07 and voters 0.87056\n",
      "11624 unoccupied precinct found at -76.4474666299935 35.984448043726445\n",
      "11639 tiny precinct found at -79.0614730009329 35.07195664231226 with area= 9.3e-07 and voters 5.94338\n",
      "11678 tiny precinct found at -78.95950574722319 35.002775893082934 with area= 5.5e-07 and voters 2.73291\n",
      "11694 tiny precinct found at -78.96430116628005 35.0591414168708 with area= 6e-07 and voters 1.92934\n",
      "11761 unoccupied precinct found at -78.89811923108864 35.00895999716097\n",
      "11767 unoccupied precinct found at -76.50367163881793 36.007421997490106\n",
      "11793 unoccupied precinct found at -76.73136507919533 34.6705717420585\n",
      "11865 unoccupied precinct found at -76.61110348502494 36.01927977033442\n",
      "11871 unoccupied precinct found at -76.63750364514998 36.02078665992386\n",
      "11910 unoccupied precinct found at -76.66041719295153 34.691676915705806\n",
      "11913 unoccupied precinct found at -76.42905894140878 34.69371992246851\n",
      "11914 unoccupied precinct found at -76.47414343891272 34.7132640616474\n",
      "11986 unoccupied precinct found at -77.01471041185145 34.63578855170457\n",
      "11988 unoccupied precinct found at -76.5585166219201 34.61111099998258\n",
      "11989 unoccupied precinct found at -76.55811584239912 34.65907377569543\n",
      "12342 unoccupied precinct found at -83.42958961047633 35.523620570855975\n",
      "12355 unoccupied precinct found at -77.87145474565912 35.61105660548755\n",
      "12383 unoccupied precinct found at -76.43070488677442 35.22522230168645\n",
      "12409 unoccupied precinct found at -77.56855910083584 34.37586734669175\n",
      "12439 unoccupied precinct found at -77.71970145335597 34.2628972484809\n",
      "12455 unoccupied precinct found at -77.24545565707278 34.552316450170416\n",
      "12555 unoccupied precinct found at -76.00804461791546 36.03323718748089\n",
      "12613 unoccupied precinct found at -76.25196057375662 36.008600109235715\n",
      "12615 unoccupied precinct found at -76.34886668762745 35.965023117040694\n",
      "12711 tiny precinct found at -78.64791591414803 35.786919711412445 with area= 8.6e-07 and voters 164.31929\n",
      "12712 tiny precinct found at -78.65012058741588 35.78193142385017 with area= 6.6e-07 and voters 73.68071\n"
     ]
    }
   ],
   "source": [
    "#ID the low-area and low-vote precincts\n",
    "for p in range(nPrecincts):\n",
    "    if vtdGeom[p].area < 0.000001 :\n",
    "        print(p,\"tiny precinct found at\",vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,\"with area=\",round(vtdGeom[p].area,8),\n",
    "              \"and voters\",round(vtdTrump[p]+vtdBiden[p],5))\n",
    "    if vtdVAP[p] < 1:\n",
    "        print(p,\"unoccupied precinct found at\",vtdGeom[p].centroid.x, vtdGeom[p].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8b420398-21c7-4d46-8aa7-0f0c3aff52fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#buffer the linestring and point-containing precincts if they are tiny  #should probably do for all geoms to avoid intersection errors\n",
    "minBuffer = 0.0001\n",
    "minVTDarea = 0.000001\n",
    "for p in range(nPrecincts):\n",
    "    if vtdGeom[p].area < minVTDarea :\n",
    "        vtdGeom[p] = vtdGeom[p].buffer(minBuffer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        if p%10 == 0:  #would overload if we printed them all\n",
    "            plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 300\n",
      "working on tract 600\n",
      "working on tract 900\n",
      "working on tract 1200\n",
      "working on tract 1500\n",
      "working on tract 1800\n",
      "working on tract 2100\n",
      "working on tract 2400\n"
     ]
    },
    {
     "data": {
      "image/png": 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/AfMjy8cDTuDUyDcA40RklIg4gRuBf/ZV8ANBWU0L8x56nen//Qr3/WM722vCX/WGOltO2u7+66bw8TnhKq/bLinkweun4dDh/5SKOSmuJGaNyOLNff3XzLI3VTdDgKci9fQ24DljzNJI4n5SRLYDPuAWY4wRkaGEm1EuNsYEROQuYDnh5pVPGmN2ROmzJBxjDDc/sY6K+jaSHTb+sPYQl9qSwAlySrPPwtwUHrx+KncvGEdOynm001dKRd28cbk8umIPNc0+svvh7/WMRT5jzDZjzExjzDRjzBRjzPciy33GmE9Els0yxrweWV5hjFncZf+XjDHjjTFjjDE/iN5HSTyt/mBHT5Mbv7UQgFqTGlnZfS94g9OTdSBvpWLcvHG5GANv7e+f6hvNCDHM40xi4pB0CnM8pLqSWPrFefzsU1eFV7ZY092pUur8TRuWQVpyEm/2Uz29JvoYN6Mgs6NUP2VYBqNHRO5t91CiV0rFviS7jYtG57B6bzUnN1SMDk30MW7FrnDrmsa2cKsbnKlgc2iJXqk4N29cLkfqWjl0ouXMG58nTfQx7ESTt2Mc1xRn5L65CHiy39sHj1IqrsyLtKFfvS/61Tea6GOYNxAC4Lvvn3Ryp2TubK26USrOjcpNYVimmzX9UE+viT6GLdseHqzbeepj0u4saNESvVLxTES4ZGwOb+2vJhiKbj29JvoY1eIL8Oire5g4JJ2Pzyk4eaVHS/RKJYJ54/JoaAuwrbwuqsfRRB+jXi+ppNEb4NtLJr63UzJ3lt6MVSoBXBIZm/mNPdF9SlZHmIoRy3cc4/sv7iQv1cX8okFsKavHbhMuHNXNIN3tJXpjYrpLZaXU6eWkuigemcXrJZXcvWB81I6jid5i9S1+Xtp+lG++8C4hA2U1rbxzuA6Az146Cnt3I0O5syHoA18zuFL7N2ClVJ8qLszmiTdLo9ptsSZ6C1U2tnHZj/5Nmz+E3SYs+9I8Rman8OVnt+Bx2fmva4q639ETHiqQ1hpN9ErFuenDM/AHDbuONjKjIDMqx9BEb6HnNpTR5g9x77VF3HThCDLcDgB+dfMFp9/RHUn0LTXhQVWUUnFrWiS5byuvi1qi15uxFnjncC1/WX+YP6w9xMVjcrjzijEdSb5XOkr02sRSqXg3NCOZ3FQXW8rqonYMLdH3s7KaFj72q7UEQgan3cadV4w5+zdxd6m6UUrFNRFh+vAMtpXXR+0Ymuj72b7KJgIhw/0fnMKSqUPIOpe+qN1Z4VdtYqlUQpg2PJPXd1fS5A2Q6ur7tKxVN/1s46Fwcr5ifN65JXnoTPRadaNUQhg/OBVj4GB1c1TeXxN9P6lq9LLhYA2PryplwcRBFGR7zv3NkpzgTNMSvVIJYmROCgAHT0Qn0WvVTT/YWlbHR371Fv5guD+Lhz487fzf1JOldfRKJYiROeGCX7S6LNYSfZSFQoZbf7e+I8nfenEhOamu839jd7aW6JVKECmuJDLcDiob2qLy/lqij7KHlpdQ2+LnnqvHc+cVY7t/0vVcaMdmSiWU7BQnJ5p9UXlvTfRRcqC6mWc2HObxVaUUj8zi81eMPblP+fPlzoaaA333fkopS2V5HNS2aKKPG8YY7vzjJkqONbJw0mB+duPMvk3yoKNMKZVgslNcHKlrjcp7ax19H6hp9hHqMnDAH98+RMmxRr5w5Rgev/kC3M4odFTkzoa2eggF+/69lVL9LtPjoF5L9LGhoc3PoeoW3tpfTYsvyKETzfzflgrmFGZz76IJZLgd/G7NQYpHZvH/Fk54b1/yfcWdBRhorYOUbroyVkrFnWjlC030Z+HxVft54KWSbtetP1jDh3+5tmP+s5eN7vvqmq669mCpiV6puOcPhkiya6K31KZDtTy0bDfJDhu3XjyKBRMHUTQkneMNbQzLdPOX9Yepb/WT6koiL83FB6YPjW5AXXuwVErFvUDQkBSlwuEZE72IJAOrAFdk++eNMd8Rke8CnwXax8D6hjHmpW72Pwg0AkEgYIwp7pvQ+0cwZCitauLLz24hN9XJK3dfToans6fJ1Lxwf/C3XTKqfwPztHeDoIleqURwotl7dr3YnoXelOi9wHxjTJOIOIA3ReTlyLpHjTE/7sV7XGmMqT7nKC1y6EQz7/vZmzR5AyQ7bDxxy+yTkryltESvVEI5fKKFuaOjUw17xkRvjDFAU2TWEfkxPe+ROJ7fVE6TN8B/XjWOG2YXMCzTbXVInTzaVbFSiaLNH+RoQ1tHnzd9rVfNK0XELiJbgErgVWPMusiqu0Rkm4g8KSJZPexugFdEZJOI3H6aY9wuIhtFZGNVVXRHRO+NUMjwwuYjXDoul68sHB9bSR7AlQ62JC3RK5UATjT7MAbyM/qge5Ru9CrRG2OCxpgZwHBgjohMAX4JjAFmAEeBR3rY/RJjzCxgEfAFEbmsh2M8bowpNsYU5+Xlnd2n6GO1zT7u+OMmymtbuX7WMEtj6ZFIuImlPjSlVNxragsAkJYcnarhs3pgyhhTB6wErjXGHI9cAELAb4A5PexTEXmtBF7oabtY0eYPMvP+V3ll53GunzWMxVOHWB1Sz9za341SiaDJ6weIyqAj0ItELyJ5IpIZmXYDC4ASEemaAT8EbO9m3xQRSWufBq7ubrtYUvTtZQBMHZbB/3xsBq6kKDzV2lfcWVp1o1QCaIyU6FOilOh7865DgKdExE74wvCcMWapiDwtIjMI18EfBD4HICJDgd8aYxYDg4EXIk97JQF/NsYs6/NP0Ue6dhH6989fbGEkveTJhrrDVkehlDpPTd72qhuLEr0xZhsws5vlN/ewfQWwODJdCkw/zxj7zc9f3wfAsrsvxWGPg26A3NlQscXqKJRS56k5kugtq7oZSP6x5QgARfnpFkfSSzrKlFIJob3qJjVKJXpN9F1cODqHsYNSrQ6j99zZEGgDX3SGH1NK9Y/2qpsUpyb6qBuc7oraUF5RoQ9NKZUQGloDeJz2vhuB7hSa6COavAHWH6ghPyPZ6lB6T7tBUCohlNe2RPWhTE30ES9sPsKe403cftkYq0PpvY4SvT40pVQ8K6ttZUS2J2rvr4k+4khteAivC0dlWxzJWXBr1Y1SieBIbQvDsrREH3XDMsNVNlEdLKSvuSPdC2nVjVJxq7HNT0NbQKtu+kNhbrjXuDV746g3Zb0Zq1Tcax8QXEv0/cAfDAEQMnHUA3OSCxwp0KJ19ErFq/ZqYy3R96G3S0/wt03lJy1r8gZ47N/7yUtzcf2s4RZFdo482rGZUvGsoj7cpHtoFBP9gBoz9tCJZm58/G0g/DVpTmE2f3unnB8t3011k5dHPjodZ1KcXfu0YzOl4lptsw+A7BRn1I4xYBJ9MGS4569bcSXZ8AZCHQm/3U9vnMF1M2K07/nT0RK9UnGtptlHmispqv1rxVnx9dx96ZnNbDhYy/3XTWHRlHyAju4OfvChKXxg+lArwzt37mwt0SsVx/ZXNTEyN3pt6GEAleiXbjuKx2nnY7ML+NjsAqvD6Tue7DM+MPXGnir+sfkID31kWnz0yqnUAGGMYfuReq6elB/V4wyIRO8NBAG48/I4euq1t9zZ0FYHoRDY3pvEKxvbuOXJ9QCMyPFww+wCHl62m0DIkJ+RzKA0FzmpTrI8TnJSXGSlOMhOceJ22ImMI6CUipKK+jZqW/xMGRbdHnMHRKKvbw0P05UZxZsdlnFngQmFk73n5Kd6m7wBfrlyf8f8T1bs5Scr9gKQ5XHQ7AviC4S6fVtnko1Mt4MMt4NMT/g1w+08aT7T4yDL4yQ7pfMn2RHDI3IpZbHKxjb+vO4wBVkePnzBcFbvqQJg6vDMqB53QCT6hkiiz3BHZ+BdS3Xt78aTTasvSE2Ljx8tK+EfWyoAGJWbQpbHwTuH6wB46lNzuHx8HsYYGloD1LT4qGn2UtPsp7bZR02Lj9pmH/Wtfupa/NS3+qmoa2PX0UbqW/0dXap2J8VpJyvFSU5H8neRneIgO8XVsax9fU6qk1RXkn5zUAnPGMMf1x3mgRd30eoP1zCsP1DD0m0VTBuewbRhGVE9/oBI9HUtkRJ9Iib6Lj1YhrJGM/G+947UeP3MYdx5RbjaKqlLHb2IkOFxkOFxMCryZHBv+IMhGlr91LVGLgyRnxNdpmuafVQ3+dhzvIkTzV7a/N1/c/A47QxOT2Zwuov89GQGZySHX9OTyUlxMmdUtl4IVFyrbGjjq3/bxsrdVVw6LpdvvW8SP3t9L89uLGPa8Awev7k46l2vDIhE3151k56IiT5Sot+x/yDve6yyY/H0gkx+esMMAiHT54OpOOw2clJd5KS6IK93+7T4AidfEJp8nGj2crzBy7GGNiob2th0uJbj9V58wc6LQtw2e1UDXihk+MPag/zktb20+YN877rJ3Dx3JCLCYzfN4tGPhXDYpV8KMgMi0Te0JWbVTZs/yPeWH+EB4IlXNgKXMSzTzRv/dcVJJfdY4HEm4XEmMTzr9M3IjDHUtvj5wp/eYW3pCeaNze2nCJXqO1WNXr7y3BZW762meGQWD31kGmPyTi5w9efDmbGVDaKkoTVcp5wepfEYrfLEmwdYujf8+HSWNLHynitY87X5MZfkz4aIkJ3i5IvzxwLhpqEQ7uGvJvIEoVKxbGtZHUt+vpr1B2p48Pqp/PWOi96T5PtbYmW+HrTfjE1LTpwS/ZG6Vh5evhvBQwgb356fD2dRzx7r/KFw53LVTV5qmn3MffA1Mt0O1n79qqgNt6bU+Vqx8zhf+PM75KW5eOHzlzBpaHSbTfbWwEj0bX7cDnv89WNzihZfgD+sPcS28jpeevcYAAYbNk9Wwo0y9fTaQwzNSOajFxRw55824QuEqGz0smLXca6ZHN2HS5Q6F0u3VXD3M1uYPDSdJ2+dHb6HFSPiO/P1UkNrgHR3/F7T/MEQf1p3iMsfXskPXy7pSPILJg6i9IHF4ZY3CdbfTVWTlzGDUnl0xR7eLq3h4Y9MY1imm9+vOWh1aEq9xwuby/nPv2xm5ohM/viZC2MqycMAKtGnx2G1jTGGl7cf4+HluzlQ3UzxyCyuKhrEMxvKGJbp7myWlYA9WAaCIVbvrWb13mpuv2w0Hy0u4ESzjx++XELJsQaK8mPjK7FSu442cO/z73LhqByeuLUYjzP20mrsRRQFDW3+uGta+db+ah5atputZXWMG5TKbz9ZzFUTBwHwpQXjyE9P7myW5cmGhiMWRtv3rioaxI6KBi4ancO91xYBcENxAY++uoen3jrIg9dPszhCpcLdq3z52S2kux384qaZMZnkoReJXkSSgVWAK7L988aY74jId4HPAlWRTb9hjHmpm/2vBX4K2IHfGmN+2Eex91pDa4Dc1Pjo/mBHRT0/WrabN/ZUMTQjmYc/Mo3rZw0/6QbkkIxTBihwZ8Ox7f0caXR99rLRuJ1J3HThiI7PnpXi5EMzh/HC5iN89ZoishKxSwsVV36yYi8lxxr57SeLY666pqveXH68wHxjTJOIOIA3ReTlyLpHjTE/7mlHEbEDjwELgXJgg4j80xiz83wDPxsNbX5G58V2i5SymhYeeWU3/7elggy3g28unsjNF43sXd8xCdgnfVqyo+Np3q5uubiQZzaU8ezGMu5IxE7qVNx4a381v35jPzcUF7Bg0mCrwzmtMyZ6Y4wBmiKzjshPbwdWnQPsM8aUAojIM8B1QP8m+tbo1dGHQobDNS00eQO0+oO0+IK0+gK0+NqngzT7ArRG5lt8QVr9netbItuW1bRgtwl3XjGGOy4fc3YPd7mzwN8C/jZwJEflc8aKiUPSuXBUNk+vPcRnLx2tTS2VJR58aRe/XlVKQbabb79/ktXhnFGvKpQiJfNNwFjgMWPMOhFZBNwlIp8ENgL/zxhzahu/YUBZl/ly4MIejnE7cDvAiBEjzupDnEmzL8if1h3i2Y1lCGATQQQkfFwEIDJvs8lJyyWysr06vH3scBFw2KRjvMczcSXZ8DjteJxJuJ12Upx23E47g9KScTvtLJg4mE9dMor8jHNI1B0dm9WAI04HUDkLt1xcyOf/9A7/LqmM+ZKUSjxv7avm16tKuX7mML62qIhUV2zWy3fVqwiNMUFghohkAi+IyBTgl8D9hEv39wOPAJ86ZdfuilvdfhswxjwOPA5QXFzc228MvfLAh6ayr7IJgyHyD2MMIRNO3AbTkcA7lkeWhbdtDzv8cUTC2wWChkDI4HbauWxcXiSR2yOP+4en3ZH5qJY8u3RsRnriJ/qFkwaTn57MU2sPaqJX/arFF+Dev29jVG4KD1w/NW665T6rS5Expk5EVgLXdq2bF5HfAEu72aUc6Dqc03Cg4hziPC8fuWB4fx+yf3XtqngAcNht/MeFI3jk1T3sr2qy/PFyNXA88soeympaefb2uXGT5KEXD0yJSF6kJI+IuIEFQImIDOmy2YeA7pp9bADGicgoEXECNwL/PO+o1cncXapuBogb54zAYReeXnvI6lDUAPHO4VqeXHOAT8wdwYWjc6wO56z0pkQ/BHgqUk9vA54zxiwVkadFZAbhOo2DwOcARGQo4WaUi40xARG5C1hOuHnlk8aYHVH4HAObOyv8mmAPTZ1OXpqLuaNzeLv0hNWhqAHAGwhy7/PbyE9P7niuI570ptXNNmBmN8tv7mH7CmBxl/mXgPe0r1d9yDPwSvQQvnficcbP12cVv379Ril7K5v43a2z47JzxAHR103Cc7ghyT2gSvQAjd4AqXH4R6fiS1lNC4/9ex+Lp+ZzZdEgq8M5J5roE4Une8DcjG3X1OYnLcHGGFCx5wcv7sImwjffF/vt5XuiiT5RuLMHXom+LUBaHLRhVvFr9d4qlu04xl3zxzIs033mHWKUJvpE4ckacHX0Td5AXDysouKTLxDiu//cQWGOh89cOsrqcM6LJvpEMcBK9MGQocUXjMsbYyo+PLnmAPurmrnv/ZNwJcX3TX8tDiWKAVZH39QWHge4tLqJ7UfqSbILSTYbTrsNR5LgtNtwOewkJ9niegxdZY0jda38dMVeFk4azPyi+H/6WhN9onBHEr0xdHTMk8BCkT4r/rGlgn9sOf3D1kk2wZVkI9lhJ9lhx5UUuQg4bJ3Lk+y4HDaSkyLLIxcJV2R7Z5INh91Gkk1w2CPTdsFhl8hyW8f0qLyUuBzoRnX6/tKdGAzfiYMOy3ojsRK9vwXe+jnsWW51JP2v5QSYIHgbIDnD6miiLivFydIvzqOysY1A0BAMGXzBEIFg+NUXCOENBGnzh2jzB/EGwq9t/s7l3kAQrz9ETbOvx23ORX56Mq985TJN9nFq8+FaXt5+jC8vGM/wLI/V4fSJxEr07QZPtjoCa6QNBdfAGWJvyrAMIHoXNWMM3kAIrz+EPxTCH7mQ+IMh/JHXQKh9PrzuWEMb9/5tGz9bsZdvLUmM0uBA8+NXdpOT4uTTcX4DtqvESvTfrbc6ApVARKSjuudsbD5cy+/fOsgNswsYNzgtStGpaHhrfzVr9p3g20smJVSLLr1LpVQfu+fqCXicdr77rx0Y06c9bqso++XK/eSlufiPC/t2TAyraaJXqo/lpLq455oJrNl3gpe3H7M6HNVLOyrqWb23mtsuKYyrLoh7QxO9UlFw05wRFOWn8YMXd9HqC1odjuqFx1eVkuK08x8XjrQ6lD6niV6pKEiy2/jedVM4UtfK+3622upw1BmU17awdNtRbrpwxNmN1xwnNNErFSVzRoW7jy6tbqbkWIPF0ajT+d2agwhw2yWJ09KmK030SkXR0i/OA+DHy/dYHInqSZs/yPObyrl2Sj5D47jjstPRRK9UFE0ZlsG91xaxYtdxVu6utDoc1Y2Xtx+lvtXPTQnW0qYrTfRKRdmn5hUyKjeF7/1rJ77AuT1tq3qvzR+ktKqJ3ccaCQTPfL7/sq6MwhwPF8XZOLBnI3GeCFAqRrmS7Nz3/knc9rsNPLnmAHdcPsbqkBKKNxBk7f4TvLarkpV7Kimrae1Yl+yw8cX547jz8jHYbO/tA2pfZSPrD9bw9UVFSAL3EaWJXql+cOWEQSyYOIifv7aXD80cxuD0ZKtDims1zT5eL6lkxc7jrN5bRbMviNthZ964XD4yq4DhWW5sNnj53WM8vHw3/9pawb2LirhywslDAf79nSMk2YQPXzDcok/SPzTRK9VPvr1kEgsfXcWDL+3iJzfOtDqcuNTQ5ucXr+/j92sO4guGGJzu4oMzh7Fg4mAuGpPzngedPjhjGP/cWsEjr+zhtt9t4F93zWPq8M7+kd7cV83MEZnkprr6+6P0K030SvWTkTkpfO6y0fz89X3cdOHIjuaXqnde2FzO/Ut3Udvi48OzhnPrxYVMHpp+2ioXEeG6GcOYNCSdhY+uYn9VU0eir2/x8+6Rev5z/rj++giW0ZuxSvWjz18xliEZydy/dCehkPaD0xt7jzfywcfW8OVntzI6N4V/3TWPH390OlOGZfS6Xn1Ejof05CRW7anqWLa29ATGwCVjc6MVeszQRK9UP3I77dx7bRHvHqnn75uPWB1OzHvncC0f+dVaymtb+db7JvLnz86NdE99dlxJduYXDWL1vuqOjubeOVyL025jekHij9+giV6pfvaB6UOZXpDJw8tLaPEFrA4nZq3aU8V//GYdmR4HL3z+Yj5z6WicSeeesi4ozKaq0UtpdTMQ7k560tD0uB8Ptjc00SvVz2w24b4lEzne4OVXb5RaHU5M2lnRwO1Pb6QwN4Xn77iYguzzH+lp4cTBOO02frSshKffPsSGg7VcVTTozDsmgDMmehFJFpH1IrJVRHaIyH+fsv4eETEi0m1Fl4gcFJF3RWSLiGzsq8CVimcXjMxmybQhPL5qP0frW8+8wwBS3+rnjj9uIsPt4KlPzSYvrW9axORnJPPlheNZvuM43/6/7cwZlc1nLh3dJ+8d63rT6sYLzDfGNImIA3hTRF42xrwtIgXAQuDwGd7jSmNM9fkGq1Qi+dqiIl7ZeZwfLdvNozfMsDqcmGCM4b/+upWKulaeuX0ug9L69nmDO68Yw7yxuVQ3eZk3LheHfWBUapzxU5qwpsisI/LT3lzgUeCrXeaVUr00PMvDZ+aN4oXNR9hSVmd1ODHht6sP8MrO43xtURHFhdFpfjp1eAZXFg0aMEkeellHLyJ2EdkCVAKvGmPWicgHgCPGmK1n2N0Ar4jIJhG5/TTHuF1ENorIxqqqqp42UyqhfP7KseSmuvj+0p0DftjBZduP8cNlJVwzeTCfnpeY3QVbpVeJ3hgTNMbMAIYDc0RkGvBN4L5e7H6JMWYWsAj4gohc1sMxHjfGFBtjivPy8noXvVJxLtWVxD1Xj2fjoVpeendgDjtojOGRV3Zzxx83MXloOg9/dHpC9ztjhbP67mKMqQNWAtcBo4CtInKQ8AXgHRHJ72afishrJfACMOe8IlYqwXy0uICi/DQefHkXbf6BNeygMYb7l+7i56/v44biAv56x0WkJyfeCE9W602rmzwRyYxMu4EFwGZjzCBjTKExphAoB2YZY46dsm+KiKS1TwNXA9v79iMoFd/sNuHbSyZRXtvK7986aHU4/SYUMnztb+/y5JoD3HZJIT/88NQB0abdCr0p0Q8B/i0i24ANhOvol/a0sYgMFZGXIrODCbfS2QqsB140xiw736CVSjSXjM1lwcRB/OL1fVQ3ea0Op1/8cFkJz24s464rx3LfkklaXRNFEos3gIqLi83GjdrkXg0s+6uauObRVXxsdgEPfGiq1eFE1R/WHuS+f+zg5rkj+d51kzXJ9wER2WSMKe5u3cBpX6RUjBuTl8on5o7kmfWH2X2s0epwombFzuN89587WDBxEN95v5bk+4MmeqViyN0LxpGW7OD7LyZmc8udFQ188S+bmTIsg599fCZJA6gtu5X0LCsVQzI9Tv7zqnGs3lvNyt2J9TxJIBji3r9tI8WVxBO3zMbj1OEw+osmeqVizM1zRzIqN4Xvv7gTfy8Gt44XT609xLtH6vnO+yf1Wf81qnc00SsVY5xJNr6+qIj9Vc38Zf2ZupGKD0fqWnnkld1cMSGPJdOGWB3OgKOJXqkYtHDSYC4ancOjr+6hvtVvdTjnxRjDd/6xHWPg/uum6M1XC2iiVyoGiQjfWjKRulY/v3h9r9XhnJfXdlWyYlclX144rk/6lVdnTxO9UjFq8tAMPnrBcH7/1kEORkZFijfVTV7ueX4r4wenctsl2lGZVTTRKxXD7rl6Ag67jR++XGJ1KOfkd2sOUN/q57GbZg2oboFjjZ55pWLYoPRkPn/FGJbtOMbbpSesDuesNHkDPL32ENdOzmfc4DSrwxnQNNErFeM+c+lohmYk8/0XdxIKxc9DVD9/bS8NbQHuvGKM1aEMeJrolYpxyQ479y4qYvuRBv6++YjV4fTKutIT/HpVKR+fU8C04ZlWhzPgaaJXKg68f9pQphdk8vDyElp8AavDOaOn1h4kO8XJfUsmWx2KQhO9UnHBZhPuWzKR4w1efv1GqdXhnFZFXSsrdlbygelDcTu1f/lYoIleqThxwchslkwbwq9X7edofavV4fTorf0n8AVD3DinwOpQVIQmeqXiyL3XFhEy8PCy3VaH0qNVe6rI9DgYm5dqdSgqQhO9UnGkINvDp+eN4u+bj7CtvM7qcN6jsc3Psh3HuG76UO2COIbo/4RScebzV4whN9XJ/Utjr8/6jQdr8QVCXDVxsNWhqC400SsVZ9KSHXxl4QQ2HKzl5e3HrA6nQyhkeGhZCUMzkpldmG11OKoLTfRKxaEbZhdQlJ/Ggy/vos0ftDocADaX1VJyrJEvLxyvrW1ijCZ6peKQ3SZ8632TKKtp5fdvHbQ6HADe2F2F3SZcMyXf6lDUKTTRKxWn5o3L5aqiQfzi9X1UN3mtDoct5fWMG5RKerLD6lDUKTTRKxXHvvG+ibT5g/zPq3ssjcMYw7byOqZrdwcxSRO9UnFsTF4qn5g7kmfWH6bkWINlcRyuaaGuxc/0gkzLYlA900SvVJy7e8E40pIdfH/pLsuaW24trwdg2vAMS46vTk8TvVJxLtPj5O4F43hzXzWvl1RaEsO2sjpcSTYm5Gu/87HojIleRJJFZL2IbBWRHSLy36esv0dEjIjk9rD/tSKyW0T2icjX+ipwpVSnT8wdyei8FH7w0i78wVC/H39reR2Th6brKFIxqjf/K15gvjFmOjADuFZE5gKISAGwEDjc3Y4iYgceAxYBk4CPi8ikPohbKdWFw27jm4snUlrVzB/fPtSvxw4EQ2w/0qD9zsewMyZ6E9YUmXVEftorAh8Fvtpl/lRzgH3GmFJjjA94Brju/EJWSnVnftEg5o3N5Scr9lLX4uu34+6raqLVH2R6gdbPx6pefc8SEbuIbAEqgVeNMetE5APAEWPM1tPsOgwo6zJfHlnW3TFuF5GNIrKxqqqqd9ErpTqICN9aMpHGNj8/fW1vvx13W1n7jdjMfjumOju9SvTGmKAxZgYwHJgjItOAbwL3nWFX6e7tejjG48aYYmNMcV5eXm/CUkqdoig/nRtmj+DptYfYX9V05h36wJbyOtKSkxiVk9Ivx1Nn76zunBhj6oCVhKtfRgFbReQg4QvAOyJy6rPP5UDX0QeGAxXnGKtSqhe+snA8yQ47D760q1+Ot628jmnDM7DZuivXqVjQm1Y3eSKSGZl2AwuAzcaYQcaYQmNMIeGEPssYc2pXehuAcSIySkScwI3AP/vyAyilTpaX5uILV45lxa5K1uyrjvrxSquamTA4PerHUeeuNyX6IcC/RWQb4cT9qjFmaU8bi8hQEXkJwBgTAO4ClgO7gOeMMTvOP2yl1Oncdkkhw7Pc3L90J8FQ9B6iqm7y0uILMijdFbVjqPOXdKYNjDHbgJln2Kawy3QFsLjL/EvAS+ceolLqbCU77Hx90US+8Od3eG5jGR+fMyIqx1l/oAZA+5+Pcfp0g1IJavHUfGYXZvHIK7tpbPNH5RjrSk/gdti164MYp4leqQQlEu6zvrrJx29WlUblGJvL6pg5IlOfiI1xZ6y6UUrFr+kFmSyZNoTfrD7AJ+aOZFB6crfb1TT7WL23iv1VzfgCIXyBEA67kJfmYsm0oeRnvHe/UMiw93hT1KqFVN/RRK9UgvuvayawfMcxHl2xlwevn9qxvLbZxx/WHuL1kuNsO1JPe8eXTrsNZ5INfzCENxDigZd2Mb9oMF9bNIGxgzo7Lauob6XVH2Tc4NT+/kjqLGmiVyrBjcxJ4T8uHMnTbx/i0/NGUZjj4ck1B/jZa/to9gWYWZDJ3VeN5/IJeUwdloG9S3v4QyeaeXZDGU+/fYhvvLCd5z53Ucc6m2i7+XihiV6pAeCL88fy/KZyPvnEOrJSnOyoaOCqokHcu6iI8YN77lp4ZE4KX722iLRkBw8tK2FrWV3H4CKeyADgrb7YGJxc9UzvoCg1AOSkuvjcZaOpqG+joq6VX31iFr+9pfi0Sb6rmy4cwZCMZO744yaO1rcCdJT8o9lOX/UNLdErNUDcfvlo8jOSuXpyPhnusxvAO8Pt4MlbZ7Pop6v5y/oyvrJwfEdLG58F/d+rs6OJXqkBwpVk56PFBWfesAcTh6STl+biaF24RF9e2wJAToqzT+JT0aNVN0qpXpsyNJ01+6pp8gZ46q1DJNmEK4sGWR2WOgNN9EqpXvv0vNEcb/Qy94HXePrtQ9w4p4DBPbTNV7FDq26UUr02b1wuT902h7+/U07RkDRuu2SU1SGpXtBEr5Q6K/PG5TJvXK7VYaizoFU3SimV4DTRK6VUgtNEr5RSCU4TvVJKJThN9EopleA00SulVILTRK+UUglOE71SSiU4MSb2uhgVkSrgkIUh5ALVFh4/Vuh56KTnopOei06xdC5GGmPyulsRk4neaiKy0RhTbHUcVtPz0EnPRSc9F53i5Vxo1Y1SSiU4TfRKKZXgNNF373GrA4gReh466bnopOeiU1ycC62jV0qpBKcleqWUSnCa6JVSKsFpoo8QkRki8raIbBGRjSIy55T1I0SkSUTusSrG/tLTuRCRhSKySUTejbzOtzrWaDvd74WIfF1E9onIbhG5xso4+4OIPBs5D1tE5KCIbIksd4jIU5Hfi10i8nWLQ426ns5FZN00EVkrIjsi58T6sRaNMfoTvk/xCrAoMr0YWHnK+r8BfwXusTpWq84FMBMYGpmeAhyxOlYLz8UkYCvgAkYB+wG71fH243l5BLgvMn0T8Exk2gMcBAqtjtGic5EEbAOmR+ZzYuH3QocS7GSA9Mh0BlDRvkJEPgiUAs39H5Yluj0XxpjNXbbZASSLiMsY4+3n+PpTT78X1xFObl7ggIjsA+YAa/s/xP4lIgJ8DGj/RmeAFBFJAtyAD2iwKLx+1c25uBrYZozZCmCMOWFVbF1pou90N7BcRH5MuErrYgARSQHuBRYCCV9tE3E33ZyLU3wY2JzgSR56PhfDgLe7bFceWTYQXAocN8bsjcw/T/jCd5Rwif7Lxpgaq4LrZ6eei/GAEZHlQB7hwsCPLIsuYkAlehFZAeR3s+qbwFWEf0H/JiIfA54AFgD/DTxqjGkKX7wTwzmei/Z9JwMPES69xL1zPBfd/TLEfVvl050LY8w/ItMfB/7SZd0cIAgMBbKA1SKywhhTGtVgo+wcz0USMA+YDbQAr4nIJmPMa1EN9gy0HX2EiNQDmcYYE/k6Vm+MSReR1UBBZLNMIES4Pu4XFoUadT2di8i64cDrwG3GmDVWxtkfTvN78XUAY8yDke2WA981xiR01U2keuYIcIExpjyy7DHgbWPM05H5J4FlxpjnrIs0+no4FzcC1xpjbo3MfxtoM8Y8bFmgaKubriqAyyPT84G9AMaYS40xhcaYQuAnwAOJnOQjuj0XIpIJvAh8fSAk+YhuzwXwT+BGEXGJyChgHLDegvj62wKgpD2xRRwG5ktYCjAXKLEkuv7V3blYDkwTEU/kQnA5sNOS6LoYUFU3Z/BZ4KeR/5w24HaL47FST+fiLmAs8O1ISQXgamNMpQUx9pduz4UxZoeIPEf4jzgAfMEYE7QuzH5zIydXVQA8BvwO2E64Sut3xpht/R2YBd5zLowxtSLyP8AGwlV5LxljXrQiuK606kYppRKcVt0opVSC00SvlFIJThO9UkolOE30SimV4DTRK6VUgtNEr5RSCU4TvVJKJbj/D3zJ4au7UjQ2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic NC map\n",
    "#tractMAP = wholeMAP  #uncomment ONLY IF WE ARE STARTING OVER\n",
    "tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%300 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-84.5,34.8)\n",
    "pt2 = Point(-82.6,34.8)\n",
    "pt3 = Point(-82.8,36.1)\n",
    "pt4 = Point(-84.5,36)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #in case we want to clip southern tip\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  80 tracts w pop 251761.0  to reassign to tracts...\n",
      "[254, 702, 703, 861, 867, 932, 933, 935, 936, 939, 962, 1109, 2098, 2100, 2101, 2102, 2118, 2119, 2125]\n",
      "I have flagged  449 precincts to reassign to precincts...\n",
      "[2504, 2505, 2508, 2509, 2510, 2568, 2713, 2714, 2715, 2716, 2717, 2883, 2893, 2894, 2897, 2900, 2975, 2976, 2980, 3166, 3167, 3168, 3169, 3170, 3202, 3203, 3208, 3216, 4053, 4054, 4055, 4056, 4058, 4570, 4571, 4572, 4573, 4834, 6426, 6427, 6428, 6433, 6738, 6739, 6741, 6742, 6751, 6752, 6755, 6759, 6762, 6774, 6775, 6776, 6777, 6778, 7062, 7770, 7771, 7772, 7773, 7774, 7775, 7776, 7803, 7978, 7979, 7980, 7981, 7982, 7983, 7984, 7985, 7986, 7987, 7988, 7989, 7990, 7991, 7992, 9904, 9905, 9906, 12665, 12666, 12667, 12668, 12669, 12670, 12671, 12672, 12673, 12674, 12693, 12694, 12695, 12696, 12697, 12698, 12699, 12701, 12702, 12703]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            x,y = tractGeom[t].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V/N5YiKhQE/L2W3iNHVunnSRJ2E6lofT1AYWSsvnzKfriC6q37wBA26EDgU8/hS4ujtIf51H4yScIahWRc+ag69gBfbCatAdfJePx12g97HoEZfOE/xy3FOLpdGFQN1x2MOWYW0Zv37B6z0t2O+Ur3A6HYZ9/htK7zlblPwI5142MjEyTqKioICX1HUpKfnAfsIPisEhpwCjGTXkPheLCithZXo6juISqTRspmT0HR0EB6qgobGlp6Hv1IujFF9G0bHG6fe7k3pQdLCfs+dswTnm0Wa6j0+xOhAhq1ty4r97zObnJXL30KnytahbfGo/KYKy3XfmSJeQ89TTGYcMI+/ijZpHtYpBz3cjIyDQal8uFxWJBpVJRVlaGxWIhJSWF4uJiqqqqyM5OolfvhQCUVwZgyyzCv6MLT2EFc6ev56YXj1xwDoWXFwovLzQtb8Xziiso/OQTHLl5+N19F57jxtVJP6Bu2xEObkMV3rCZpSmUl2cAkCPV7/suSRJvz78fs1Hi3d7vNqjkAbzGj8dy4gQl33xLxZq1eF4+ollkbE5kRS8jI3OasrIyvv/+e4qLi+uc8/T0pKamhsjItqePDej7Pf7+MWQU7CPpt0mEx1lZ+tUtjLv920bPqQoKImT69PO2UWjdqsqZm9bocc9HXv6hs/5/mKDATqc/O+w2nvriatabsrneHEubLoMvOJ7/gw9Ss3sPuS+8gKFXz9MVr/4pyIpeRkbmNAsXLqS6upqhQ4ficrnw8PDAw8MDPz8/fH19T7dLSNBSWPQ8CYcvp1/fg0QEdKOm1+dk7r8Lj5Df2L79S/r2vZOt6z8i9dAPXHb1FxzcNQdP7wgGjXi4STI5slPJnfUbCBLakTc3y3W2aT2WV1LX8kJ+PLN3zODJCT+dPrd4xbus8UzjuqoOPH5H4x5YolpNwKOPkHHzNMyHD+MxYECzyNlcyIpeRkYGAJvNRmZmJr1792bABRRV587Xs2Hj8wDs3fsO/fu/TNuI4ZSWvUpNxvNUn3ybZUcP4mFaS2gcnEidiN7fna5mw7Iaho59ttFymTcsAMDQyhuFV/Ntdl454kPWzu7B6tIkHnM62LD6Cw6n72Kl8xBBksBTd3+PqFI1aqzq6jL27l1GALBy5/dssv6Ij9aHl/u+jFL8+9WsnKZYRkYGSZLYsMHtC96+feOCk6JbzgagqvoXHA4HAH06X092eUfEMAl9i7U4NZBRJCK5wFYmIKkhv/RYk2RT+rtzyRj7n5tz3lVZRvXSb3FVlTVpvNOIIr18O1EkQuzcOB4t+pJZhoNoBBXPtHuw0Ure5XSy5eZxBHyyCIB9advYmr2VpSlL2Z6z/eJka2b+/keNjIzM30p5eTmzZ8+mpKSE9u3bExER0ah+UVH9OX78MlSqTWRn7yIysh8AN05Zwo8Lr8NcdIDug95gaJvx2BzVLPiuK8FqJ32GPtYk+Rz5+QAUL1yLcWIiRdMfpmxXJpLTvWGr0L5F1JzvUHduen76wV1u4d14t9fNbVJvbpv0LnqtscFasOlHdnFkxVyw2nCUleGz7Qg+pQ4igRPXdCe0x2AeHTgS7xPf88PRH9ApdU2W6VIgu1fKyPyfs2zZMg4cOMCoUaPo3LkzGo2m0X1LSjPZvXskkhTMqJHrz9t2xc8xaP0lbAdE8h0mbn5yT6PmkFwu8m8ZRunO3+MzJbxi/VGFBIGgpGjFQfxGdcT//V8aLffZlM1/jYoT7+JrvALDg3Pql8HpxG63smtYH/yKznjqnGrlibNlKKqWLRh+/1soanPk78rdxW1rb+Opnk8xpd2Ui5KrqcjulTIyMvVSUVHBoUOH6Ny5Mz169Ghyfx/vcJC6oFbvwmotRaNp2IZeauiKX8F+1HEuIvNLsDvtqBQXNo8IokjgZ7/Cg5NBocTrhrvQ9R99+nx1QldK4w/jnZ2CMjS6ydegVnoQ4XBQZTPXOVdTXszWG8cSdLKUSqMCvzIn6Y9eTd8xdyCICtoFhtbpI0kS69LXAeB9nvvxVyIrehmZ/yBms5mCggKMRiNKpRJBEDAYDO5EYRYLarWa7du3s3fvXlwuF4MGDbroufz9+1NWvovNW6YxfNivDba7YYy71uv3X7UmNMRJUdpRgqM7N2oOQe9J0Fd1Sl4AEPTCi5y640kKnrqdkO83Nln+n04kcAuww2JE9eQ0agK9UEdE4DyejHb9LsJzzWREGZAUIuY7rmDkLS+ed7zMykx+Pv4zAJ6ahqNu/0pkRS8j8x+jqqqKn376iaysrHOO63S604r+d6Kjo+nfvz/eFxm6n52zn4LCuajVIIqHMZsr0OnqKrf0ol3s2HkzUoWDIH8X2EES67eDNxXtgPF4dniN8j05BJnNiLqm2cWz1UawQK4pn64/pqI4y5qdF6Ama8oghj/7GaLYsO9KubWcU+WniA2IJaUs5fTx/qH9m3w9lwJZ0cvI/IfYt28fK1euxOVyERcXR2RkJA6HA5fLRUZGBqIoEhwcTFVVFS1atCAmpuGsjI0hNXU5anX+6c8HDnxG375P1ZUr+Wv89TZEO4hVAlm5MQyZ0KlOu4tFHRUFiQk4s44itura6H4uh4P7S9JxKQUiw8cQtO8ahIJiypKPoGnRgnYxsRccw+6yc9Oqm0gpT+HJHk8S6RkJQNeAxstxqZEVvYzMf4TS0lKWL1+OSqXi7rvvxs/P75zzPf+QJbI56N37SXbv1mKxfInVaqRz5/pL/UUEjqA8bSOCBWrs1zDl8debVY6qPUcBEIx+F2h5BkmSyH97IsHKTZyq6kKLnhPw0/tBlB++UW0u2P9o8VG+S/yOVWmrTh97c8+bABjVRt4dXKdEx9+GrOhlZP4jGI1GJEkiNDS0jpK/VKiUGqqq0lAqITr6Bfz9Wtdp43K5iAjoyZaiiZhYgHLDQiTHq82WhRLAd8rVZL/3E7l3XEPYwi0If6jrWh8FO7cSbN1EUs1w4qvuIvzIMSJCghs1X2ZlJtcsvw4B5+ljM/rPQKPQYHFY6B/aH1+d73lG+GuRFb2MzH+EN990ryYb8gG/FBQUHEWpXIPN1o3Wra6st828Tf0IEgowugARNC1c5CcnENSu+Uwbnne8iCUxgeK1Ryj//AVMD7zRYFtHTTk500cToDwFIpwwDwREenZpnCmpxl7J7XvWUBQxC4C3YnwY6aslQB/QDFdyaZAVvYzMf4SAgACys7MZMeKvy56oqHWP9DC0r/cBY7aW4kcBUjF4HhSxR0gUH1Tgd2Pduqt/Fv93f6I0riPVu/ZhaqBN3p5nOVE8D+dlIKY6KT05kjx7W6zaKtR/iIR1OG1klx8hIX0e5ZVJKJzl2F0uJEHJQeHD0+2eOFlCqTOYB6Oa/ZKaDVnRy8j8R/AbOJTPjqSycPdRjIoT2EWRFiqRYLWSII2a9iYjV7RqgfI83iNNxWSKxGo14XTOY8cOLZ0734nBcMaDZ9ueDyk+6IvHfgnxpI2gD2bQ7/bR5/VguVikqiJcTgF1SGC956tOLSOpYh6oBVw1Ro62VHLC6ospsorbRo1DpT6jDistRazeMgAfhQ09oJHAJgkoBIlPeBKNZGFRXEuMak8G7j7G+uIKHoyqf95/ArKil5H5l5NrtTEvt4SvCqpxmnwwmauxiyK4JHa7lFicaqgBairpm7mLhUN7N5t5R6FQ0avnL+zcNZUa81ds3TYLSepKhw6PEB7WnYItB8k9WGvSiAL/V97A+7Moglt0bJb5f0dyOin7dAZIAo7sk+ecKznyJTkZsyhQ5SO6IDbyRUq927Lv4FTadlyDXVrD/K3voTHG4q0PpmXgCLYdfoJghY1sZSeiQ8bSp+U0AOJzDrAvWcHtXifp5u3OvRNr1GNQ/LPThskpEGRk/sVUOZz03HmEEruTDh5aPmoXSQePM37kkiRRbbGSXFrGjEPH2ar14nlvNffGNi5xWWOx2cycPLmSjMwFiOJ+RNGJ09EDlTmIA/OSCenmR86+IhROF+3Lchmx/kCzzS25XJS8MI2CBbsB8O9cgd9PmSCKVKQuYl/qYyBBsNCalnHvofZ1X7vdYSUx61cST31BkCuDP7r1FwpBTL5s2znHJmxdzFG7L3v6dsFT4wXATYdTOV5tYUevdn/p/sgfkVMgyMj8B8kwW/k0o4ASu5M5nVowws+rThtBEPDQaYnTBfGjvy/d1u3iTYeGz1dtZ7JRxV1d2uFn9PjTsqjVOtq3v5r27a+mtDSd3btfRBB3UFmlAqJo3bUvJ5OXYqiwEZBnobIkH6NP85g6yj988rSSjxmfh1LrgukBFAeYOBztROES6NV9KRrfc98iVEoNcVGTiIuaRIW5gNKaLHLKj5Kdv5Jw/8sY3PLmc9rnVeezzx7KeI9sPDVe1NidHC+tpotRz5qiCvaUV9PT9Ofv5aVAXtHLyPzLqHA4eS8tj6+yCnFKMMjbyLwuLREbsZpcn5rOy8fSOak1IgkCgZZq1vXvQkAzKPs/UllZwLzXr6MyRceN773JN3PeQ3Eoj04ZhZg1LvK8FXh068A1T3yKVtW4aNbiF6ZRvm4nTpsLcD/I7FXu69YFKYhavx+WP0TWgTV4U0lux2DCL5uLxu/PB2fdtGMh682RLGir5edEkaW7MnFVOxA6+yCF6tnduz0BmsalNr4UyCt6GZn/EJ9mFPBFZiFdPfVMjwklzlPfaJPBsJaRDGsZid3p5Pmte5mlNdB570ne9NVwU+d2zSpnTmo2FSk6tIG++Id2YNINjzHn1OMkhZ9xQzQfyueHK8cAEi1KK2jVcwChb7yB2EAGzeq9SVhLwat7BIgCOOzYCsrBacVnynWgVLPRNIFbrFcQLBRxq48n07z/vIfPmsxdrLFEM1qZxdRv1Tiq7GhMGqyiA1dCCQGC+Lcq+QshK3oZmX8QG4orWFNUjs0lMcjHSBejnpb6M0rP6nKxIK+EPiYDi+NaXfQ8KoWCm9u2YNaRHADa+tQ1+/wZJEli1RdfAjD0lgcBaBHejic+ms+uA2vx9vRHEmDFmy/iEhQoXQLBJ6upWrWao1u20GrpUtQhIXXG1USGUJN+jODZqxAUddVXZXkZM9ZlAYHYNUqmr1NTUj2fJ8ZNvuhrqbRV88BJKwFUQmkojqocxg1ryQdD2nC0pJobvt6NJbmcGpsDvfqfqVL/2VvFMjL/J8SXVHDFvhNMSUhlTk4xC/JLuPtIOn13HeXN1NzT7XaUVZFttXNNkM+fmi8xr4CxB1JQOR18EeJJr7C6SvVPIYGmsgqt2kS72DOBUTqtnsF9JtClQz9i2/fj2dnreezntdzwzfekBrrVkVhVTcrlI6morXh1NqJOg+QUkKznphSWnE7WrVnGlW8v5pTTn+ndzex6fiIA3+8Bp9Nx0ZcyN2Ur5XhyX5Cd/enliFoFHw5tiyiKdPAz8vm1sVSZ7Tww7wD5FZYLD/g3ICt6GZm/mTK7g9sS08i12nksKohDfTuQOrAza7q35upAb95Pz2fk3hMM33OcKQmphGlVjKpn47UpfHUslUqNjjmtQ5jQpmUzXckZBFHA7FJhc3kguS68D+jnGUBJWBcAbEOHgMNB9r33kfPU07hcrtPtLKnpqL0ERL2Rk0cPUlJcBJLElzM/5PZNIhkOb74bLnLDxIkoFBomd86h0m5k0e5NF3UdBTUlvJ+nI0bMpqN3N8pyqunS1u8cU1nvlr7cf1kMG48VMOy935j23W4WH8g6z6h/Pf/M9wwZmf8jVhSWU+V08VOXaLp7GU4f72LU81G7CLxVCn7IKSFGr+G+iEBuCfXDW3Xxf7qzDx1hoUNJoL2ayyKaeSVfi8VcicqSi6fFRmbKKSJanf9hsn9zPB2SjlLi402fjz/Gevw4GTfdTPmvv2I+fJgWS35FKszAt0MKO1u149EXv+KgNQTIrh2hDSN98njjphGYAsNPj9slIoSfEsBDq23yNbhcLu48sJUqKZS5bX1ZcKwMgJvjwuu0fWREGybEhXLTd7vZdLyQcB89V8aFNXnOS4W8opeR+Rs5VWNlekoO4Vo1sUZ9nfMKQWB6qzBSB3ZiXY82PN0ymMA/uen3eU4pLkFkQa/mT0PwO9WSmho/BUVGPb+89CCf33z+bJUnVq5GZ7EgXXEFoiiia9eOVtu2ou3cGVtKCqfGXIH9vX7oVTaGGA5R6dJyuSmbm4PS6a7NwV8o5/Xbxp+j5AFigqIRcPHjzqYVJAfYkrufHbYI7vI+Rc/ADuxPKwGFwKjo+hPGtfAzEGrSoVMpuGNg878l/RnkFb2MzN9AjdPFuuJynj2RjSDAnE4tUJ6nEEdzBuKEC07SRJGHdx+myiVxa3gAUzu1bbbxAXz1Gm5//TXW/fAiubvV1FgTqM7NwRBc/xuEYNAhAfa163A9/jiiWo2oUhH50zwyrp+C+eBByjVKtHHu9mufn4hCfVZgmMuFUE9ahR4xbRnRajtrkkNIKywiyr/xWT3nZGYgEsk9bYexK6eMk8kl+AQZUCsV9bbfeKyAnaklTOwWRqBn098gLiXyil5G5i/CJUnsLa9mVnYR/XYd5c6kdMocDuZ3iaadR9OqIv0Znu3UinbmCo6IatJUWh4vsvDC1sYV6m4KPr5dmPTAr6T5pHNK48N9773D0eOn6m179VNPk9ClM0H5+eyfNQtz/DLsp45BdQVePdypg8tP6dnteTkVGM5R8kC9Sv53buzbBgEX9875lVHvfMfNX36D3dHw5qzZbuGuXQtYYW7JRI80dAojN811x/V8OrFLg/10KvcDYMG+LLq+so6nFx3GYnc22P6vRF7Ry8j8RSwtKOOuI+kABKlVTG8VSqxRT8d6TDaXktjQYDaFupVnpdXKhE27+dplYFRWLn3CGpePvSl0HPoC03e4///bt4d5tl8ut47re06bTbO+pdWJZAAM771PWu1xUe3CZRNReQkEv/AcFYd/oFThR1MqsfZt25exbT5gxYloNAoLR4sM7E7eS792veu0LTaX0WFnGhDDBH0Kb8eN5ZENx7CUWJk6qhV9gk0NztM10psR7QOJ9NUTf7yQebszOJRZxsoHBzRB2kuDrOhlZP4CbC4XX2UVYlCIrO/ehkidulGRrJcao0bD820imZxWwqr0nGZV9BXVFuKmb8ApQZiinOeGRTJjVSqvb5OIP/wTvVoF0Dc4icLyz4g4qxiTykvE2L01NUmpqAM88Rx3JR7XP4QgihgOfIJV0CBJUqPNWYIg8MFND/KOS2LFvngeXmwhs6T8nDYFBcdZtOhr5vn0A/8oAD7tPoH1p0pYuS0DnY+Wlwecv+yiVqVg5o3uwNSnRklEP7OSI7kVTZL1UiErehmZS0iG2crHGQVsKqkgy2Lno3YRtNDXH/X5d/Hp8XRQe9A3sHkrIrX/dgdKCdSixMLHxhLobSQsOIQ7Z+1kb7maLfvNtBB0XBXcHu/hh6EwgAD9VBT+MaDJw3NENbpOwejHjj09ZlVIf3pkfM32n9+i7+QnGy2LKIqoRRBFt8rzMWjZsWMWJ09mUF5eRVGRHlHUUu2vJ8BaycMh/rSesR5njQNJhFeu7Njo1MoOl5PxMxcDWnzt5ZgrytF7mZpy65odWdHLyFwCimwOfskr4d20PJySxABvIzNahXH5n/R/vxQccYnEWKsZGRPXbGM67HZUFgsulcCJV684fbxj2xZse6MFTqeDJT98w1WpT0AJFEWrKBhzJaE+MVTvysRepsZc0Q7Ltmp0Q6sQ9O5cPN1ufIPEt/fR+dj72B2PompiOUKzzYaAxLZ1yxCq3WOKopqWLWHQoIms3VtCUXwFL1OBpBQYNTiK6zoGMyiscQFqkiTx2Yu3kGS/FkSJe1uG/u1KHhqh6AVB0AKbAU1t+wWSJL0oCMJLwO1AYW3TZyRJWllPfxPwNdARkIBbJEna0SzSy8j8A0hLOEDCulUYfH1Z7BnKAu8wVIKAXZLob/Lg/XYRhGsvXMP076KDwsVmtZH3dh3kkV6xzTLmk3c9h+Q/kF4eqfWeVyiUDKpZevqzX5kdzfG9qB54E1MP9zHLuqUUbfCm5N3v8X3uLhAERKWKkuBBdEw7QG5OGsER5zen/JERXXoxb/1PCNUedOniwfDh0xBFJXq9+wGsW7r+dNuPbuvJ+KjGe+kcPZjEI7OOc1R7LUMVu7jr2tH06FJ3H+DvoDGPQyswRJKkKkEQVMBWQRB+L3v+viRJ71yg/4fAakmSJgqCoAb+2p0nGZlLSH7qSZa99zo2cw3Lhl3LMW93kIyPSsGXHaLo/Q9NW/s7kiRhrg1cNSmax4587FgCG7174ek0M+ehO3C5XDy95SRrEnKprrShEAVEpYiq7CYOKbeyRHU5l1OOsWQntm0rUfcbDYB2+Dj0SfOpyeuI41QyypbuwuOR6QvIVEYSHtaiybL5eHjSziBhr1Iwfvwjdcwxq+8ZQvSzbvV2RZip0eNu2xzPg6tLKdGoGVJWxZcfv4DyTwS1NTcXNDpJbqpqP6pqfxqV21gQBE9gIPBN7Vg2SZLKLk5UGZl/FjUV5Sx5ZwYKpZLbPv6GSF/3673GZuFuqfIfr+QBVqWks0djZJyzimndOv/p8ew2G8+9tYoKUcnNoUq0HhoeWHeUn1clU1lixsuoRhAFaorMlDsNzHcMYoRtE9ZJL+AUAlCsuwtXSeHp8TyGtAKc5M3MJ/31zzj8xggipSzSI64EsX5/9vPxxZItaKtyceh867W5KxQid/d0f4/bD5+sc74+fti8nHvW52BR2Pi4i5Fvv5j0j1Ly0Eg/ekEQFIIgHAQKgHWSJO2qPXWfIAgJgiB8KwiCdz1dW+I27XwnCMIBQRC+FgTBUE87BEG4QxCEvYIg7C0sLKyviYzMPwZrTQ1rPv+AyuJCht1+L14Bgcy+agyfe4K3tYZXLGoeW7qKnPLyCw/WTLhcLuzOpvlt/5TmTiHwTNcOzeIZcvlbc9kb0BGnqGCNTxD9Zm5l9Z5sEOHYcyPYe/8gjj4xlFOvjyZ1xiiWR16NiAvLxgdxDZqBgnLs8545PZ66cxwBk91mFUV5J8Jr8tntM5ZOYx9osmz7TmSRvv83rGpPnr3vlgbbTe7fHgUuHvgliV+2JJ53zC/Xr+DZlQIe6mqe7PERV1w/qMly/RU0StFLkuSUJCkWCAN6CoLQEfgciAZigVzg3Xq6KoGuwOeSJMUB1cBTDcwxU5Kk7pIkdff392/qdcjI/CVILhdHtmxi/itPk7p/D30mXk+rnmd8wq/sFsuGy3rQoaaUucZgeu06znfbd51nxOYht6KSJz6fyTPvvk9KcUmj+0WJ7pfzo6UVzSJHatWZqlHHjxSTnVqOo8pOUIQnqrPqqgqCgCiK1LT35pvQ8QTmJFPjlYdV1RNVwUqkswKNtla5V9hJJheKRzfR84G5eHk3zUMoKz2ZZT9+jUZwMO26iXgaGo5cPXQyk+7KTCpcah5fkU7rp37lWG7d+/PDlhW8ucFJJ78kXuz9Fj3bvMKaNWuYM2cO3377Lf+k4klNioytNbvEAyMlScqvfQC4gK+AnvV0yQKyznoDWIBb8cvI/CtZ8dZzrPrkXYoy0hh66z30veb6OithX6ORtWOH8lOwjsCaCp41K3l2+WosNtslkelwbj7vfjkTXVE+aouZz2fNIb+y6sIdgUAPt3nJZrU2iyxOf7frqK2jN5YRITiDdLTr4s/aaXU3Je1OO4ds3uxuOxiLXoNm7asoHSdwCXo466GwcOUJALQ1Ikq1sUnySC4nez6eis+3/U8f2zH/Q6SzMmL+kR07d9JOWcDV/gVEKMqwoWT8p9uosbqjaS02M498P5tnV0CMTzF3xy5GIbpYvfondu3aRWpqKhkZGSxfvpzc3NwG5/kruaCiFwTBv9ZzBkEQdMAw4JggCGdHVlwJ1HnHkSQpD8gUBKFN7aGhwJE/K7SMTINUF0Fl3qUZe+v7WE78BkC3dt7EjhjdYFNBEBjctg2ze3bEp6qcbwxBTFixCYvN3qwirT2WzA/ffYvaaqHP1dfSfdwEdFXlvD1rDpXWCz9Y4kLcK/Cn0ouYefg4tvOkBrgQWVlZxARVEtFLRYy3HQQBexcffr6yC571JGJ7cN8KKjEyNDgM25g30ZptKKQyJNGOcFben4+nD+VACwPRNtj57m5+PprDwmN5ZFSY2V9YwavbU8ipqvugysw4ReLbl9OjeCmJPkO5a9pkQlRVZJq1SK76TVwWqw1leTZO35a8/eitbJ4xhWu7h2N1uHhxaRIbEn6j3+uLWZTkx4R2ufx462CUUgk2q4l+/a7m6aef5uabb6ZnT/e6t7Ky8qLvZ3PSmB2DYGC2IAgK3A+G+ZIkLRcE4XtBEGJxb8ymAXcCCIIQAnwtSdLvfwX3Az/UetykAtOa9xJkZGo5thJ+uQmcNmgzGiZ+B6pmTC51dDlXRyWzznwZexKLCFn6JTHj7jxvlw4hQSSOD+KpVeuZZQqk8/pdfBLpy4gOf75s33fbd5Oyfg2SRss1U68nLjwUgLKqatLXr+bV2XOZfstNDSbhAugbFsw7ldW8fqqKF4rMvLFxHz+0j2h0hKzdbqeyspINGzZw5MgRBkkSkyZN4rI8t+IdaK3AU1s3QGxR+j4WVUdxlf4EU6InIooiFZNEPH9+AIvGzua736PnS1MxBvojiiJRQyLgm6O0qnLxwuxDZHLuivy7dScxeWu5rH0Aj7c3kDHvEWIrNhKKi22tHqPv9c8iiCLVThEPwUZJbjp+4XVdM4+kZqEQJILDI08fe35Me+bvzeKXfVn8sg/Ai7euqCFQSmfXzkmo1U7atfuAqCh3qoOoqChWr14NQHh43ZTGfwcXVPSSJCUAdSIpJEma2kD7HGD0WZ8PAvUWrJWRqYO1Clx20HmDuRRKToF3FGi9wGEBhxX0tcErNSVQkQ0uB2x+B46tAN8YCO8FB+fCzEFwyxrQmf68XDkHIXsvwogZDOt2Cym3XEXy1nUXVPS/8/rIobTcvot3ql08cTKXYe3aNDrS8o/YHQ7eWrQU+5EErN5+PHjjDYR5m06fn9a/N+9XVcHOrbz0489Mv2Hyeee6oV0ME1uG8+Who7xRpeb942mNUvQJKQks+n7R6c8qlYohQ4bQrl07Plac4qHMUnYpdBwrLqWtrzfb85J49kQKoUobv1kjaCHk8nbcqNOyCUGXs6GvL4oTcbTx6kHe23tJM1nQhHhhLtTgDWxpb+TyqCDMSCQXVFFtdRLpo2djYh5RpTsZLc6k5nuJLjUVHAq+hoDBd9Gv7Rn11b99KGsSC/nsm9lMGzuQ8G7Dz7mmXUluv39f7zOBbR4aJX4eSsprarC71MSYUjCZP8OhtAOBeJseP63kfycvLw+TyYRO99clqzsf/ywfIJn/Pi4XFB4FUyRoat0P8xJhzTNupV5T5F6RewRBZQ5If7ClikpoPRJCYuHAXChNcx/X+UDf++CyZ0GphYjesPwhmDcZpv7qHqcsHUpS3Q8Dgz8oVHBqs/vh0WIgWMrBVgWBneB3xVhVCHu/gQM/gNYEcTcgavQYtCIWc+Pt2oIgcEe/3lTFb+UtyYd5+xOY0j22ybcvraSUz36Yh764AEtMO16edBV6VV2zyMMjh/FqdTXqwwd47dflPHfVuPOOq9VoeLBnLL+s3MxepQazzY5Off689y9veYlOnHHJHDhpIH1i+gBwTesWhGvVXHs8h1t3JbJ8WG9ya4o56ozgqBPaKHL5qlM7DGfZ3HPS1oFSwLNzbxxtNFi3FKGvMKCpUqGvXcGPurodJkPdN4Q9XSUqkmYCcKqHgEF4gR6X1fXM6THxQfz9f2DWpmQKs07WUfQ927fg18Tt7IlfzbCurTEZ3YXX9z53OT9vXYqX+RFEwYXVEk5suzcID6+7Nblv3z4AysrKznv//kpkRS9zaSjLgIPzoOg4FByDylxoOxoyd0PRCbfCDuoMljK38tWaILKve+Wu8QRrhXslH9jBrcwtFWCvAUmCI0vg2HLwioBWIyCsB/S60933d7pOda/0lz8E77cHu9ndvzH4tQGfFm45UuOhuhCCu8AV74DOhL2qjLIaiZCQpm0MAtzVuwcz1+3ktSqJkZWV+BobP8aqpGPEL/kVlcNO4GUjuGtgn/O6RD575Vieq66GhP18YDDw0OVDLzhHZ6XASZWG7ZnZDI2OarBdeXExHTebsSq24zmwL9YU+GHhD/R/ov9pmXpHhHJ1aibzlEY6bkmgBZUgevKA6QTjCvdRsu1L9gqeqBW+qJV+ZNvmgRa2fb8OZ1k8AEqXC0HjRazPZdToDAzR92dv4SmyS3ZxRcwElAotB4uSyU6aiognkUWFFPmqKcl9k8Cdauh9Vx3ZHZL7IW7wruvd1619NHs79yU3YSt7jp1ieA93cRan08XB7Vm0CWyN0hDIpPGfoVLVH+18+PBhBEHgkUceueD9/quQFb1M8yNJsPQBSN3kXpmbwsE70r0CN0XA5a9BRQ6cXO9e2Xe6BrrfAsagxo1/+QywVoL2Aslqu09zP3Cy9rgfGJ4h7geMztt9PGE+lKQAAgx4FHyjwVYNB39wm4TMSW4FP/hpCOt2etiTS7/E7lLQZtCoJt8avVbD3V4qXnfq+XHfQe4f3LgUtom5+exY8DNOjY7xU2+ib4uIC/YRRZGXrp/Ec9/MQtqxhe88PJjWr9d5+9zZqTWLjuXyTUpWg4reYjYz58Un8axWAAqsaw9BdBdESayTqfHt/t3peuAwK4urOK6W6CwdIKpgEfmqI6AEtSWUSuEgLrEatKDPiSVM8EAdoEYQBKwOBxUVZewqWEpbqZhZGxcQJRzGA/gh52usyhD87EdQ46R93C+09G7D4YWtyfeH8q1H6BaThsrv3OsIatcb197DzDiSRM8lu2hraMewV9x+9cUV1aQcOYRaEmkVfub38cOvZqOpKaCIW3ls4oTz3kOdrohr5/1EwvL59NuTdN62fxWyopdpXpx2iH/dreRHzHCbU8Ct/AuPu5Xt7wr68hkXN4cgXFjJ/86wFxs+l7weSIGHEsF0Vn3Pnrefd8gTu3fgoXYQNvi6xsnwB27q2Y3XdxxlbUkVdzkdqBQX/jMM9PKkwsMLQ00VRkXjbftqpYLnbrqBV2Z+Ter61fxqMDAhtmOD7bsEB9LmwDF+UxvYf/gwLUNCOJlwgKM7t2O3WqjIzcJRUYbgctH68nFYzE4yNq/AaYUAAnjplRe57KohXNb5MgCUSiVTe8QxFbDZqlm/vj8ardsnXV9+I32udH8/DpuZjOe+QyV2IDLsKJqIYJxV1XjeMJ7iw6ms/+VjdgfHMvDwZuyxgylUu/C27MfpKKdSEURU1P209HY797Xo9j4Fh5+luusKtn6rYdBjb56zR6HU6Knxd3FVegbdVTeDDZJeWM1v6mXk44XOriam9zCigs746lfkuesITBh2bh59AJfdSXZiGicPHSMtP5M80ukM+FQ27ML5VyMrepnmwVIBR5fCto/c5pqOV0Ofe8+cFwQIaN5ydX8aqdbFrokRoWazFaNOgdDEzIm/Y9JqGFOew3LvEF5a9xszRl7YpOKv13HD9dcx/7vv+OHHH3ngzjsIOWsD9nx4ajU8Pu0m3p75FXuW/YrJoGdwA8W6lyckki5qEJ1OVr/5Emq7lbPvjgSgVNFryi0UHSslY487MZk+uwxbUCus+kI2L1pLyi9JFFoVTLmpBxEd3L4YarWBESN2ciTpV3btXoiP7UxwlShq0GjaItlFKpcvpLwii509nqMm0R2CY3MVMFT8lQFlVohPoCxuBIZhG1HqfeqYrwyRozFueY9KQyWl/unEfziPT8JLuDn/GDtCJ3KLlIiiqIzuNTfjVFiI91iLd6GJ9trBVJm2ENr1aqaM6n/OmD1GXMXONYv55ocFvP7EPe57IUls+mk1e44dwCy4XVk9JR1tnF1RPD2FFtd345+CIEmNSlvzl9K9e3fpnxRVJnMeJAmS18Lqp2o3OltB/4eh87Xuzc5/Mnu+hRUPuzd3r/+50d22vXErOw/kcee7H+IR1rTsiWcTu2QjpToPDvXriMnQuFx/b6+Lp3pbPDUmH6bfdw/qJjxsUopK+PLrrxGdTibfdDOx9XjW9FyygQyjDy9U5+B7/BAIAqaQMHoMGY7T6cTkHwCAuaKGr+69Fa0xkH7X3EhMj/bEb1vFge3JONVnxQq4nKQEhtE7/yhxk6ZgzkknyNWCX3YsY1SvofQa5TZdFX5zGGtyGZKnnf1l+8ioTsdQFYFZ535IZATt4xXnu2gUZvJ8fQkqLsahELFNmYO+5dhzrmH5+s9YmXKCHPEYJZZ8btkfS40dEgap8dqfTlCJhmtaPIaExOLIH9HvGoZV60Nol/kY26zD4f0lJoOeaksZrYPbIIgayqvLmLt0M+qiAjrFdMWvRiQtM4NTigLCnL60dAVgcqkwSnp0GHAKTlTTgghu1eYvKzoiCMI+SZLq9XCUV/Qyf46Nr8KWd0HtAZN/dPuv/wMqJzWK3x9EZ2/iNoK2I6ew88B7pK6ZRedbp1/09IN0Cn5Wqbl71QbmTRx7wfZrEpKo3hYPgLqijKKqakJMjZc92s+H6264gZ9nz+LHH+bieduttKxNxOZwubhr+ToyPAPpUpbPPVdeAWOvaHCstT8uA8lCv0lTiR3mtvuPGXMtlUI8J3ZuR2vxQ2314Yh3NiFlxRSKJtb+sgKLpEBHEjrUdB3aG6vVwZaZB2ibbSGDSjZZ9uPQOYltPZChE0ehNxpYsHshQevi0WAGYGdyayb47EDpdOH8aSrVDySi0YdQYi7GqPLilW1GCszDMbbbAGr4PnY/1x4YyeCQtWSvD8VX637AHXGm0D69F/uNS/A0d8fQJY7S3EScRdMpcSlAEshNFLFYPDCbPfGuMaCo7EnyoTwStYWIopM4Rwva2wJwKVw4BAeVrgqyLAVklu+h8Hl3fdzQtu0Z+/DTGEz1pQP7a5AVvczFc3w1bHkPYm+A0W+D+l+Wgfpkbe7xXo3zhf8dn06D8FC9xfGDiWc5FzadD4YPZNeSDZwUL1xxSpIkNq1YjlIUUbXvwjXdY5uk5H8nLiyE0msmseHnH/ls1hyeuOM2AoweJObmsdzoNqXcE3n+TXFzpZXj29cgiCY6D+5xzrnrrhjMrp5xlH60gJRKHx5r58KvYxtyj6zgxQQf2mqLkBwSkQ5/DsVnwm/ZtHWCE4kgPAiXfBkxdSyrC9fz7MbnyDXnkmxLJtpDz9UVYEHBBJ8d1LiUKDVOfvCbwDN7ioAihEo7mu0FgPvhZc65Bl3IL+TrPekWNJrALVdw0vcAx8t3kyuUssPgtruLmhhqhFLy1oI78P+PuNCoa1AWdkRjDkcDGCujEVwVZAoqMlGiUFgRBAcOhxeC4Iek9kN0rMZlTyX72BESN62j15XXNvn7ai4uLmJD5v8bhw1WPgHzJkFAOxj52r9PyYN7Yxgg+0CTugkKBbGdw8gocFCUuO2ipxcEgTjRQabJj3mHzp8lUZIkVDYLynadeXHieLpERZ63/dlUVVXhcDgoKioCYEjraLqPcadKeKs2VYKzyp0bZ0R1IeO7djnveJUlNUiuUvSm9oj1RN328vfi8pemoRMryDpRgW/HLnS89hkWTr+LUIOakbZY+jhaE7QxG4PTySmxgC3KI6RELKK7ycL0zW/xbvK7bKzcyFHHUTSShtuHPc9GjyGUCgZypVDWll3FV6em8nLMGfdJRc4Z99khXgYc5d2oPPYyVace4HZnNSkdfAgJiCVQF8UKzf7TbV2CxCC206e1wMSJvRk7JJouQU48RCtKnHQ1VeFb2hG9OQK1v5kBk0z4hZWj95bw9KnBN7gcpboMUSHhLtkBgqhFZRhHP78MRvYLJm7Uhd/YLiXyil7mXCTJHbBkr4HqYvDwBwT36jd1E2TshLJMcJjdLpGXv968aQb+SnreATs/gyOLoedtTeraeepT7Nz/AAkLZjKkY7+LFuHV/j3YtvkgT9kNDI2pJsBQbxZvVh88jEKS8G3kBmxZWRnff/89lZWV2M5Kpta+fXuuvfZarozrRFn1mVQJyvAI0PpxZYsLh+wHRHojKjxRaxqOSxAUIna0nMwNYXB1BQqDJw67FYuilBLPr1lSOZL2eJKnzKZasKJSmQluu4xcIKJ8KhZHX7YNfxVBEHgncQf3pyTg4ZXFUxt7k+ptR+XIRaHy5cVlK+jfYwj6BCU1COwR1Ux4pCcGPy+Sk4sY/s0ukKBUIRFvtfLSS4NIelIkqKiCKlUpZl0+D6o+wFOoYNYRL4pyzaRUqnHhdh0FSMyPxKsyAp/AfCa/NBmAToPjEAQBW5WFrC2JZGw9TlZKMeWmMyUHBUGkh182isuvBu3fGyErK3oZN9ZKWPk4nFgD5rPS3CprlbjDAhovd1BTi0EQPQTajPr32OP/SOZuWFir3D0Cz9+2HnTBMQSbJPJyiv+UGH4+vlztoeRzhZZ5Bw7zYP/6S8/FHziEoFRzfb8Ll6bbs2cPa9asweFwIIoiAQEBqFQqcnJyOHLkCDNmzECSJNRqNZqISMhIZ79Si7e/kvGDBjZKbpXWl9LcRN56+X0yO/YmxtefCbEhRJrOvNkFBlcT7wrg6jlJuMKyOawLw9xpEkbzGKatX05+3gaqYzqBIFIpeJFc1BWVh4Of9W2pVE/g6k0LyCGWaq9wlMYQrt6TxZFunngkJwDQ68bJtInrTt7MXTglAz63RDGljbvqVMmJffzwXf5pWaLVaq4ZGEVy0iHsNVacDh16ymip92VfweNcFvg8DhSkVWppb6wizFeHR2Y8KslKkiuOfDpRkh/I7NsWICHiRIFGsFGp8EYSFEAwOk0hgsuBVFuAfJz3CyiueAPajGzUPb2UyIpexs3qp+HQPHfwkm8MqA2g93Wv4KuL3K6S4b2gET7f/3gW3QEJtV42ag8Y+VaTh7CWF1NlkdCq/7zX2k3tW/FtQgaLSst48A/n9uXkcyQnB5AQkfDV1f/2tGPHDuLj493FR+xur5eePXsyevSZDJulpaV88803WK1W1Go1NTU1kOG2U4dWFPPSkH6Nzr8z4YlHmTnzU37s1o0qgw6kKt7cd5x+DhVdNVq2llZyuFdLbCoBhVOii0XBDWTwtRRDz7SNaHNzkFx2fFXJhHY8RVHwQKKDbmdo1Ajy1q7gs/QqMo5FotCX8rp5GekaDajdq2VLRCtUBdls++ZTNosKCoc/izJIonUpiJuS0SntpCXauRYPjBYLCkMptzw1ntWLvsK26hD6oFO4bE/Sya8QfVk0IV7RbCi5mnJXEUOFJfhovYk+lXD6WoNyM/hRPwmDqxSV4EQhWVCpBRwOaGPMIbhDEIYt71OzK52gJ6ZhvGoazByM0mB0v/X+A5DdK2UgbRvMHgNxU2HcR3+3NJeWklT4KM6t4K/7CVo0LjL1bMrTj7L45UcprYYx146g1dUP/SmRluzazb3VApOYS6yXH51DR1Nl8WfF5q2IaSdRnpU7/ZGnnq6TDdJhd/DG66/jcDlRKpVERUUxfvx4jBdIr1BWVsaHb7+FpFKDy4XK5UASBNq0bME1085vyjqem8fY/ck4RZHPwn0waPz5KiGbeA8nVrWI0SrRBxVjg324oo0feq2atA96E1FylMmOp4nL30VAdUuUj5sIK58FwDfHbmB7Rt3cMZdHpTNYEHCKIqm5+bhcLjqK29AKVTzWeQZlnj51+iBJdC4vpWVOEcOPFWFTVjN9VHeKJU/840/hlFR8PS6WvPhEelZ4UmjJZ2fJtwzxO06YsZKjhp5ETn4XQeVF6uWjKff0YWj8asQG3FkLrgyj+KgRYZQHbQPy3Ck8piyEVvVt7l4aZPdKmYYpTYNlD4BX+MVHqv6bWHy3+99BT16Uks/ZvY4lH72H0ylx1dRxRI6556JFsdTUsGvnXp6ocnG3xzf0ZjuucoGi8lkcThiGojwEIaYNHVq1ImnlMgDee+N17n7kUQI93Uq88GQued8cxKFx4uvhzf2P/fGdoGFMJhP33H8/W9evoyA3lxqbjaoaM0lpmQzIziIoNKzefpVmC9fvSsSq8+D7Fn4MbO2OJejfyo/CUgtZlWY6hXmhPOvtIGXvSqLLjrLe0oKel+8jsSQO3/BiEvK2oAq9lQKzje0Zbg+euE7+nMqooKzciiPKg4ORnflyVH8kSWLfSy8hLFuOvsa9PzCyeiudr5vIqBYtsFXZyc3N4uiBD7k6dxmezmoAcn18eMpxG04UvLHlDfQKK0847uD6pYk8SSFFDif6O+O4u+0K9x6VKJ5Ot3v00AmMNjO5bTo3qOQBDnYfR/jRTSwpqebtbpFIxiBu1Crp73KgFP9+Nfv3SyDz95EaDz9OdqcFnrIANE1P0vWvIz8JEKDv/U3umr19Kb989AVGtZMJDz+Mb7em57o5m1Uf3YIh6iiDAsbSm+3ket9Bv6hJpB4Yim9QEeNueJ2Wfu7Vapi/H2tmfwfAlpOnGN+hHYfnbMEzFVzueFW6tTq/t0x9+AcFc+UNNwLumrNJu3ewcPU6tm9Yx1U31l864sX18WR7BfGMoua0kj89nrcWf28tZ1sKXE4Xa9fP4W5gsDqTnJ/C6RO+ho9r3KkQNhb9iJ/vSBztjIzflES7Ug1IFj5uo8cZqaVAlLDb7ey5/wG84+OpCAujzNuEKTuHm1csoGToALzbRbM/cTmB296kZ3U68cFDUYV2pTwrh/vSRjNa3MH+bRPRK90ZR8sx8Jrjeo5FZHDztCnodLUxFX/Yc5LKKxAAhVfDrqySJGFcuoWjUUp+uExCcpZjrHZx/8b7CfUI5ZlezxBkCKK1d+umfDXNiqzo/59ZfJdbuU9bCX6t/m5p/kIkKEoG/6b94R1d8QMKQeK66e+ij+j0pyRYMX8Bnj33ADCJeRQKIVzT6SHUCjUnHGr8vY+dVvIAfVpEsrZzd5zWGq7o1I7EOdsISFFRYjRTGixBBvj6Nq2OqtNpJz7+a3z9glApDWzcGE9xsTsjY0JqOsof+6NR+RMaOZKOPd2xBim5ufys9aNjWR73jR9R77jx+YXcnJSMEyVOQYXLJRFSNh5PpY1unqkI4Z78GNKTxX3Hcyx3A8uOfcm1+sV4rlpKevF4MjwPEWbU8XPhSF5N2kd0bg5bpz9JUGEBVf370ePLLxEVCk6cykAz6nKC77+H6rFV9DVUUCNqOTR6JkN6un3WD+9dR1reRABSldGIV36OsOAmbleuJKBNCh6hxSzeuJ0hfb4myKfuG8zx736gLdDnkTsavI8b5swgtNxB5ZXDWHn1k4iCiElj4qFND7Ejdwf3bnCnAnmt/2uMjf573CxlRf//StKv7tTBPW7//1HyR5aBrba0m9qjSV3tFcVkZJXjbVRRUKbHR1+Gp5+pySJUV1WyaflS9h49wYBB7mM236fo1/IKNEoNW7Z+hFJpw+W6vE7fF68ac/r/2lN2zKKTzs+O4IsZH2ESDLTq3b7RckiSxMJFr3EkSQJ+9045k3bXyysPrWc1giqf/KoE2C0Q1O46btidhKD34oWOMXU2bosqSrhj/a9sN8WBoEcjmfFChc4iUdMygL0h01jtr2RdjTsy9dOsMl5uPRUx71NwgmGDgvy4w9R0aEt+ZiW6o58wJCCMIVviqTZ6Ypt6A92feeZ0SoGIvKOkKdxvDhktBpATN5oO3ScSV+vuK7lcbFv5K78/ktV+1ehdu1Fcfj2sfpceUUPYagdfzSJ27B6H3u9xLu9+JlFdaWEZgQe2kxUcTbvzFGLRfP0LOaFahj78NgrNmc3ymSNmUmWr4t1977LgxAK0yr/PDVlW9P+PVBfDsgchqJM7L83/C4fnu/+NmwpeIY3uVrBvHcs+fp8yixIsEgtnPAiCintmzkXnWb/f+x/ZsGQR2/buRxIEJFEBAlitOjQaM8Pb3ohS495gTU9bRXAIdO/2bJ0xnE4nx3/dh3S8Ei+HliJvM06nk2JbOR0CYuoNXmqIxYuncyRJwtOrgh7duwASwSFtOJywDYNBy5AhT6FUatkX/w5lrs9xukq4Yd1W0rwCeF6sYmCrMxGxVQ4nMVsOuz94dwVAdLloYbXTyyASEKRnRFwHOoUEUWm38eGJnczJKWFecQQZyz7k+lVTWdjXzBU3/Ma9jh1g28EXfndQaHYxYJu7Rm/XHdtO28gr57xFxfLlVCQUotBAxCdvoh0wvs41Hty2mjyXP5/Zr+Iu1SLC8vJg4XOnz+sliWuHvMnB1AkcP/YwHuXPk1bQi6gAd8K3DW9+RgdLBd7PvdfgfczISMKv0EbalAHnKPnf8VB70M7HXTZy7pG5DI8cXqfNX4Gs6P8fObrEXfBj9M/gFfp3S/PXEdDBnWGzpnG+7xXHtrNj1gcknqrm7CBypcaEw1qG0Ei9WpCdxZYDCaBQ4qNSYFDpUAdvRKMxo7NEn1byAJFRI7HZTrBj562MHrX69PGkudtxplTiY9bjQkWph4X29w0kLzULu+AkJPDCD67q6mI2bPiG1NQyysrU6PUubpz6IH5+Z6JsY6L7nNNHqVaDBcxVFaQrtXSuKeOesedm29yakX3OZ4XTiZfDRo5CzWyXBiqcvF+axcv5Rdwa15HnOgykffpiQn+4Ds+dIgsvh0HxY3hnwEBshdF4iQWEnHJwKrIdS4IrCc2sJNLuQK9UUjHnfbJf+w6FxoWpZxjedz+Btk/dtx+A4oQ1gDdlQf34QSwm1nc/KocLP303FF6RePZ8CoDYlr0xar8i9egENu15mWlXzMblcuG9eQ3Z4W0YNrThgLhFK97mckBs37AZcEzLMby681VqHI0sfHMJkBX9/wMVuZCxHfIOu6su7ZsDfq0hrK4r23+a7FqX3W43n7dZwZ7VbPv+M1LznYhAtw4BxN30LLNfeA27tRS9ZwQVhWUsmPExpbmnuOOzj9Do6uarkSSJowf3s2jRIlCoGNarB65yFVa/exFFF0ZzT7qP+uF0e0vZUTraUqk5UUmxLv308cLUXLwSnYCe0nYCba7pSbhWjSAKpB46CUBU54bNbzU1pRw/sZWlS3YjSQrUagn/ADN33vESSuX5IzY79bqHDRs+oqh8J2U+VzPIUnrO+SMFRTxwMhctAtMDPZjYLgatWn36+tPLyskoq+D2I+m8XOBi5ftfYdZq8Wudwb0FKsBJQDG82f8gojaPLfZr0Lty8Y/wJsbly/chKSyNsbF7fi+uWKSkZXINIuB580BcSpGK9H3U2M2oIzqgD2txetXvcjjoVPArfob2xN7/EmbbFH7a8BIRmgUklFVhV2WiO/gwbSNvpH3wEErtJxAFiQjdVj7a+DF9S1sSUlFA9uR6S2OfpqUhEtiFn3f9HkoAepWeq1pdxdKTSzlSfIT2vo03sTUXsqL/LyNJsPU92PAq7kzigvvfwE4wee6Zuqj/L6TGu/8N7NBgk9ztS5j/0ReoFBK9u0bR6fpH8Ax3r9Y6Db2S/Su+JCimDRWFCeSnbAVAFIU6lZUsZjNfvv8OpTYniAquGDKYDl168PXsB+gQ4PaLjxvy9Wk7tzVvD665Y/GuMuMNhJLD8ZnTMPW5j7zfyvFBifb2VnSKPtdWvP3obgAqxFMkrF9BVlY2BQVWHA4YNao31dVFbNp0CqdTCSgwGmt49NHGB4iJCiWSSyRbVCKJCnr6ur1PCquqmfbbHg5ojYiigi9DjIxu3+acvoIgEOVtIsrbxPXfzeKUxpNDrduhtVoZn7qbzPtUPLb5TXw1NYj6VwDYHP2Lu680H6NLokIh4oXINlx0HV5JzHH3a1Rl/hKiNBKqYhscc8/nkFQ4USDg5KBO5I1Qb5xmD57KOMHchDUkJG9maLSSfqYMcAAOyDyyhfQkUAog1n59J0o/Zfiz7g9B3c7/phQc5PY6clZUnLfdQ10fYtWpVfx49Eem97/4jKcXi6zo/2NkZGSQlpZGm1YxBO55A/bPhvbj3bZ4/3ZUpu3H+vMtmD7sSp5HB9QDH0Ht6U9F7kk0Gi0+Xcei0jbO7vyvI7KPuxj4t5fDw3VLvEl2K6tmfoZeLXD9ax9hCDv3ddwvzJ2LXa03EhgzkPyTmwH46MZrQRAYOPVhelzhTiGwdtEvlNqchHp5MGzUFbRo247EjftRKBwAtDQ+iUp35j7XLL8Fr2ozpUNuR93xQcq+nkRk5kmKUmvwRUVhoIW4Pyh5SZIwO93ugnPnrnPLprbg42OnpETFkqX7QBLx9rYxYEAc1dXVxMR0bdIts9isSApQlKu4edsvGIvyWPTTfJLsTuydu3NZiwge79yaLkEB5x0nWh2EaMvj65EDsKVk8dueHvyY4q50VWzV4+XQ41LWoJIUhGvacZlOpLA6hyEtRtE97hYGzB/MkRCRzY8Gk2Up4Lmwt2g55gaK03OwZx3BlXcUSlJxWB0gKnnAsJVKlQNJfZzbNl3tFsIHfilVUX2sKy8//AWlNXn8dvB+/J0p5Eh+jOu1kIyyXDQJOykxfoVPpZ3UeZ/ScdDoBq8rpmN/skTIWPcrPSc07JlTZavC7DDjo6snuOsvQFb0/zGWLVtGi8J1BG7c5D4Q0B6u/hYUSqqzjyH9OBmdZCPbbyD+RTvRr7wZAFNt/6q1j5Db/WEixjz+d4h/aZmyEN4Id6d0qIf0NbMoNasYfdXQOkoewKM2d3tFYQE3zHiCjMSJbPlpIWV5qVgqM9n+688kntxLeumZ1d3kW+/A6OmJw2Zn37HPaNvO/RZgCjpTfUhyOdEXZFMZ0Qbvge8AYHhiOxWJCTC3HICYVmcSk/1OdVk6AxytyDMmE9U/msiojoQEd0IUlSQkrGD9+t8wmVRcd90z6HSmi7plp5Y/gtLkJG5jMn32naLE1x/1ETOta6qZuGEVdrUaW58+lD/9FF5RUfWOkbrhMCcdObQyhOMsqqBmTyr7Mg3k1ZaDvE6xAd/DrfgtvJwCz3xShURSq/1YOOZ7Fsx5isNbbkcKEtCIEtOi0/ksT0erjh1xSTZ8YyIgJgI4k09mT+JhKvfFc7X3FAa0H8ejm+/DqSgE4E7tGO574g0Agr28mDxoLcfzNtPXtys6lQedDGG8G9oDRt3PqvF9iNiWSsqeDUT3qL8KmHdIC3bHhdJuwylcDkeDQVVGtRG1qKbCev6V/6VCVvT/EXbu3Mnq1e7Nuy4B4VDgPu64dQNKhRJnTSm2WRPQShbKx39PZNzl2KpKSVv3CS5RgzGsLdayfFS7PiFi73SyPYMIHTgVyWGn4tgWRJ0JY3Td1WBF8l7MP07DYggn5M7vURmb5st9qan8/h0EpQrDpPsRlGp3Dh9rZb1tc5L2ARKtxrprxuZu2c2W7PVMnPQUoiBSlutWFtYat/KN6NiSKdPdD8QvnnmdPLWV0lolr7Jb8TWZ8KhNQ5Bw6CRhbd0P3zDxTkxhZ+5lTdYGDDYn5shzN0I9O3bG8EA2RV9uoWKrD9qYJFRt3WYna1Uped9sobUjgh5Xdcez1blmk86dr6Bz54YLhzSG4j0ryVOuQZvpQdBnWzEplXTQu236NWVlJH/7HZU7duC9eTPJCQl03bb1HJdLh9nM/vm/EJ+ajQKR3oVhFH6ZDBjoQTgVzp2s0z4PgM1Lwa7Sj8gv9EYf+TkKfTpXrxkFZ+WbW1muZoSng36eNk5mTuJEmgpqetN/2EfoDO6HxvQ1b/Fz3vcADIkZzMCY9hyI3sA1y67heOlx7r329TrX2Sao/kRuXd79gsxJ12G96wHCt+9Brak/FbfYMhJxXzZrJl9G8J33EDvc7aKZV51HfGY8pZZSAg2B2Fw2FiYv5Lnez/3l0bKyov8PUFxcfFrJA7Sf/DLpe9oSueMZ8j4chjV6JIbkJQTY80jt9y4xcW4vBbWHN1FXPn/OWPY+kzC/1RrjxqfJiX8fP0caXqITpySQbYiDqkI0CicotbiGvYx112zCpQyoyqDmrbbkm7phnPA6XtFxDcrrqqkApwvRaLok9+N3nPkZZM34BgDfXdsI+GC+W8nr6q/0U1Nehk7poqK8kvgbbidd4Vbos1MeZtqzH5KSuBS/DiX4BPtRWVbAqWO/4RfYlsCI9uSp3SYUH42Ka26YSnB4xOlxtyYk89WBldxYG0RqCBp3jj3fdmIxBkDT+qo6MilCQvG7ox/5nxymdP4RAl7ogKW0kJyv49GUhKMdq6qj5C+WqvJcsk5uxNMYRlnC9+Sr40EU6TDwU0ye50ZN600mujzyMPAwex59FI8VK0maM4fIwZfhGRVJ5q7d/LJwARV6PZ4WKz0VrdGqyhGNZXgMbMvwnn2ouHMZ39m70tOvGzplG36YPplP5x7k04Ie6PTuzWilS6StwUaiWckUHysh/o8RJbamsjyPkpJduDxW8tu6mxk0/HsUatVpJf95t2/p397tAioIAgrRbdu3OW1olBcu9AIQEtOFxDuuJPz9RWQf3kWL7pfV206pd8dkRCUWwf2vsOz69RRd1Z9PD36Ktda09jseKg/MDjNG9V8bhS4r+n85KSkpfP+9+5e7bdu2TJ7szpftPeIeThWnEnpiNurDSdhQkdz2AdoMv/W846n0nhyXIugoHEewWsk1xiGGd0d/fAHB1fspwwebU4OvMw3niluwOwVqFFqqhr6LfcvHBJXvQpo9hHT/Eaj8W6PWh+PdfwLOvAycx7fC/tloSEVyCtTouiH0mII6bij2A2tAoUY94FpE7fmLmEgOB4VbVqANaYExuh2Csv7atKJvCCoj2CuhePVh1O89gsnlQFIZcBVk4sxKoWbHBrCaMYy7kWqzHb1aYskTd1GicuJjEalSOCg/cIIVs8bg3fUo7kfEa+ze/xoA+ckaPP03n57z/qeeqVMjdM/h4/gUWCm1xuDd4SR7jt2EwW853n7+AAgZu7CpFWhD6s+9owgNRx++laqMMLLfW4Sj1IDS4YtidDX+/Qaf0zYxP5/1r71J5/49GHKNe2W58NB+nPFPULIzmLi7ptBr0EgkSaLg8HoK8lfjsNdQUVKBPXAXgkIiuxzwAW22kY6DZ+IVfn7vLF2HjrBiJco33iT1408I/OJzfli8CEGhYFzHjsReeSWi4owvqsvpZOsrz5NRXUyksQUe6iuoMJWj1ih5+Nbu3F3ThYryR5m7MgEf+yIUvtu41suPccPmo1Cc7SU0hQPbu1AivMb2DU8yZOzHhBJJNunU2MznyJhRkYGI2Ggl/ztBXfoAi8g8vKNBRd/znudZdTKJDtvcbqY+S7fzTORu+gT34ZHujxBhjKDYXIyvzheVqEL1N9RSlrNX/kspLi5mzpw5lJeXo1Qq6dWrF717966TsdBRVUJl7km8WsQiKtUNjHYuy374jrTkA/Toczm9L3e//ktOB5bSfHR+br/7wr0rcS17lEAhh4zgq4m481sAqtIOUT33FgIdJ+sd22FVYFF2QHRVoRNT6/iiO60iFnsAkl8nFP2moe0/uo7izJ31AsFpHwJgcRkpiLqbkClPoNTU/QOqnPMmWa/NOv1ZY7LjFVlDwaFzc5eovUXWdwykquLMQyb2lfco//YnMnK30+nmZAD0rsmotWFUV5Zgs2cgeKwn2Od5Vi6rwuyo4vmXn6ojQ1peMbO++BiFfwvaapfi0+Lg6XOCoKTPrgKsJn9Md52o954BuMrKKHxnIXZHa6ze6XhPbIdPdLdz2uQVZTFl3nKGnNhNmDKLEn8wFA+jUnucLvbfOF4xE4c1gase70pu6qdU6vciuJQgCUgKO+qiaLSGIRw9sISA6khGvv5Tg/KcI5vLRebGjRQ9/wKa8nJWXTEah1rNjddeS2iXLnXarrjtRk5UlxGk1NBz0oPotgsw3EjY0Nhz2zoc7F51FXZlEQNGbW9w/nVLb8SlOMrwkbsor6lk7E8TQJBYM2UlBp37+4ydE4tOqWPH9TsadU2/U1WcT1r/waT1CGXUt6tQNLCoADja1h0Y9eUokUH3TGdCzIS/rDA4yNkr/3OUlJTw8ccfn/48evRounat35tC6eGDd6um+cuHRUazLzkd65nsuAgK5WklD+DffTR0H42rpowIven0cY+oLng8t4+y1AQWv/ggYVTQt0s0gt4LIbgdmhG34VFrT3Xmp2Hd/DNSViJCcHsEnHBiNSpSUFvWwYZ1WJd64FT5IfS8Fe3oOxDUWqS8RGySjvy2z6I9tYSIjLcwv/YlBdF3EHLjmcjHip3LKP3lRwCC7plI3mcLsJapqFRq0Qaq0MWE4H3HQ5T/+A3FaxLpvrOQxHBvlBqRTHUIHSMiCXzzBX587MxKrs+wMxk+zdVVbFo7nmzbpwhMRcLFT6s24xJExg/ugaE2nXBeYQkgoTTsxjPsTJ5zD0NbbNYitJY8bHbLeb8T0WRCPfQkvpvepNog4RWdfM75NYu/ICkpmx6xRajzzeRpu9HeuYBStcCuqCAUjEOzPwdHzXoWvbWajlNPEi7eSct+9yEqtRz9ZSGG/d5U6iuoOuSFR1DjS0NWpuXj26EbWf0HYt6WjAuR6ydOrKPkHTXVLLjrFrKt1bQNjmDU2x9y7NN1qCU9Pu3qBu6d3PIB1YYkQrjpvPP7BwyguGYbyQkbaBM3nIfbPc5LKU8yb+tibhs+BQABAQ9V09JeAHj4BnLqslbEbExm05h+hD73PG36XlEn/cO65Z8QBqzoLtD3rhe5stWVTZ7rUiIr+n8hP/7oVl6DBw9m4MCBjS4W0VgKcnMA2LN7FxmpKYRHRdGyTTsiYuoG5YhnKfmz2TntbkqCvCnBgJfYnZ73PFGnjSIwCv01T/7hqFtRO7OPY/31DUTLb2jIRHHweTj4POX6MWgchZQL0YRfdz9I91G8/kdc2z8lKOVdCjd3xb5vPp5l2/AUCshOdvtBW2rriYpGA8Eff40m7ky0o3/34eh++gBBchI3/o46ewe+Q7JO/z991RsE9bkZjSkIncGDIJ97KHU+gUmXRWWVnmO7NgIwfWe8u1BIdBdys9Lo2XEDPj65ZJl7cUX/9/Hzcu8ynvpqBAJg1124BqxSAUoxH6XjXPmcVivHFiQxKGggyam5XPvKvVjsDt6Jj2Fix/bcGNoHi9XMcPsYuqeFM7z/Hnp2XIMxMPr0GB0mX0NSwq8oLEr0ooKqivILyiNJEvs+WMLuYwZ3lSVpGHQeQWBuKubEIlydnOekZdj1zptkW6sJU+kY9d4niKKIK99KtWAnMsS/zvg1VafAAC3i7j6vHO1jpxC/6TNOZU4nP38rPg6JO0ywI/tLptqvQaNS45AcmDSm847TEFd88isbv3wOz69+hdueYGWb6XR682Mi2/ZEkiRKv59L8LszAWj38HNc0ebvKwLeELKi/xdSURucMXjw4Esyvo+vH4LTQQ2QUlhCSmEJ8bv30qtjBzr16ElxXi7B4RHYrFZOHjuKX0AABg8jZcXFtI3rik6vJ2T0GJL2uwtnH9+zg6bG4CpC26C/152W15Z5jNK3r0FpsGFiOQBZnrUrJkHAd/gUzB374/yiO/4br8MhqSjS9aHM/zrgF6yd+lO9/zAIAjGbt6DQnRsNKigUGKc82qAsXULfJKnY7V1zUvMVSbtmU85VjBv0BEnmzYSowcuUz1HHjRxV2hHM5Xjb8vETqlGl7MeACx+fXACuGfo5eq3bbCRJEq96jeHb7F34ZSXw28Kn6DdhBkpF3dwK1tKj6H77nGqjHsWkn88513b7Pm40+TBbsYKPHpgFgF6j4IXLz+zHbJz6CE9UduPny7YxcszhehcHko8Cj0IjakMYRTWZDd4PgLT4I2z/KYlSfPFyFdIiXKKyxEpptYpSfSQbtinYH/8TJp2NFoMCydq3gYRTxwEYNf2t0/ObJD/MQnW9c1So9qI1t0DteX5PLrVWj7/XQxTXzMAuzgU1tHEqiDFk88q8B3l44qsA+OovziNMFEWG3f0aJVfeya4vXiVs4TbKr76JNX2i8Soy43UsB4+4OLwmXUu7bhMuao5Ljazo/2Xs3bsXm81Gt27dLtz4IukxZBg9hrgr42SlppB8JIltu/ewK+kou5KOnrfv0tWr8Tfo6NKtO7dNmszXT97/p+rK1hSUMe/FI1jUH6K3ltDPbz768AgCJp+b9EsXHEnxFb9g3r8CjyG3EdS6DSlfLQB+wRUQifPUQZAksu65l7DPPq2j7M9HUJerUB7WYi/P5ZuNx1C1SqF74E/8tmURIWob2VXtMAffzoKTv6+CPQAPNDiIVhRh9Panr0cAiqr3+S3hV0b1dJsiBEHgRHB7Onr9yrd5sxh0+HOqj8wiXReCZdTbdOgwlOpTS3FWZKJc8zIql4Q0+k20we66sQkrlnJ0ZzZtL4OZbQYzyq/++7xi5mccEzJRBg7m4xu/bvANMGRMZ2pmZdDe5zKSpIUN3g9zUTmrfsxEZRfpGlNIj0cmoDwrBYSt2sKe95dyPE3PKUcwaasLMFedRHS58FNq0Pm7V++/7w/qpPoD9ESHDoeqDIe9ArXC1KA8AHF9b8JmuxqXE0SFSHVFKdsODKKF13aOl7gfMN6a+r2tGotPUCSjXvqatEm7SLnnDlwnUnhgioKhURpmvDPnvIVJ/m7kzdh/EXv37mX58uW0atWKa6+9FpXqr9u9LysuZsualdisVgwGD46fTMbX5E1Mm1YcTTqCydsHo9GDw4cTKXe6f6cEqwVDaiKjho2hw+13NXnO+Bd/ISnfFyQXYcpchj43Fo9gU6P777/lKXTbl2AOaYtftB/VW9zBSh6XXUbohx8gqhu3Of07kiTR7vHFWJQaWnimc3Xr3XjqrKTa72XWznIUgsCcW3vSKcwLvUqBUnFGoeaXZpJ4YDAAQ4eknD5+5+4lLKmOJGdgBw7u+AHbqa1EZW8myJLPieC+xORuRwQcChHrxI/5rHAj1fEp+GbcTX8PiQRTHt5d59Kq0z20a1G/R9We75bjlSRyvE0+Y2+vv5iIJElkP+2+P0ml2+gypQ9BAwfV23bvxyvYlaSjX5caYu8eU2+b03N/uprdh9W4HNkYyn/lll/OPEAkl0T2M+45y/TFRN02AI+QM6vugv2bOVxyK76KocRe9sV556nver78aRitAtP4NM+bZLuV9we/z7DI5ivtd/va29mZuxOAB7s+yG2dzl9+8VLzpzZjBUHQApsBTW37BZIkvSgIwkvA7UBhbdNnJEla2cAYCmAvkC1J0vl/M2TOIT8/n88///z051atWjFp0iSUf/HqweTry9jrzyR4Oruufe+hZ7IHDptwNeuXLGLrgQQkjRZLSAuO7dp2UYreUuneoAzVFDL+o/Mnl6qPDu8+S2qfJUgWK2Fffkn5/F/Ie+klqjZt4tT4CUSvqvfXtUEEQeCZGIGEU1ksLA9nxyEf9mPCbC9Hp1Iw745exIbXv2oMMIVRZvVGqzjX7a+90ZMl1dBzSzy3+pu4u//XlFeXs/fHW+ievR6AsgA/tNct47e8eOac2sND+8ESFMCW6m8ZPuVW2nZ/6LzeHTkpxVSqdQwaeZ6iF2ct+DJyN3D5wLreQwAnF2xmT4ICH1cOHW66uuHxaulx70isby/jUEoo5ZoIzEVF6Pz8ABBEAc0NIeQuSsCnxo+Mz7fR5sXRKGp/t/3jBmBa2o9S3dYLzvNHBEHgyqFzWLlnKIJYAyhYffRjfEQbXcMbTmnQFJKK3Wk0hkcO58P9H2LSmJjYemKzjN3cNGYXzwoMkSSpCxALjBQEoXftufclSYqt/TnfX82DwPnf+WXq5eyqQf379+faa6/9y5V8Uxk2/iomXzUBAIeXL1k26/k7NEDfh9wPkAKL50X113gbqQrvgrY0HVu1De/JkzD07+8+WY8dvDFcdd1wnFo9kiCyzW7EbHcyulMQO58e0qCSB8gsykYUJMrt5246XhfZic6KNLIkf17Jj+DF37agUhs4FDrhdBtnUQmTf5nGk7vdD/yPJrjod3M1D3w1l3Y9Ljuvkr9n5qfc3a4V601JeIb7NdhOEEVqcO/9KLwiyN2xrU6b4/M2sma9A41kZuwrI1HpG2f+8m7r3pPw1/ui/UMVLP+O0XR+4UpKo8rwtHtz/Kt1p8/Zq8up1BxGZ2/ZqHn+SGBAKKMGrsWg9kErSKzJT+XjfY1P6HY+is3FVNYWsenk14n+of15deerrE1b2yzjNzcXVPSSm6raj6ran0bbewRBCAOuAL6+KAn/z1EqlfTt2xdBEIiLi/tLzTV/hradY9G4nABUeV1cIqfklQcB6BJtPn/DBrCWV6PNOY4jKBqt0V0Uwpzgdm/UtGqFNSMDAEdREebERJw1F84X/uhbi1hsdV+PXinw3jVd+GxKN7z0DZuBskuy2Lb7enTKKlq3PjcEP0Dvx9qBE9jR1psOJPCly0jLrYk8q+3I++E3ADAtOJAUTa1sTj0uQWRNbvofpzmH8qo8jn89gs9ynmHIkd/oJHSn5Kf4Btu7nC70uB+oZmcVBQf312lzfHsOSoeZyTMG4xHa8EPjj7Qd1Q9JcqGKaN/gQ6njnWMoUxbhme7BkSeXkvjtStL2fotTXUZMq7oeW40lyBjBnKu28sNIt1dMpOefr79w06qbGDx/MAAhHiGMajGK9wa/R2e/zjy15anT5px/Eo3yyxMEQSEIwkHcGVTWSZK0q/bUfYIgJAiC8K0gCA0tZz4AngBcDZz/fY47BEHYKwjC3sLCwvM1/b8jNjYWQRCYNWsWdrv97xan0dx1330AODwvbhOsONX9exB7R/2FJS7Eieemo3Ra0A0709841J2cqnLVKjKmTiXz3vtI7j+AtInXcKJrN0rnz29wvLJKM2uc7hXptH5RbHt2GC3D3cpxbUYxD247QdsV+/gyMeucfvH7vyFAl40p9D26t+pTZ1wAX40Hj1k+4Vbpc4LNpbSrFtC0eYwfRF9uTgzG5DJiLp2IZHUHxCkUdf90KyoyOfTbdI68F43+nTa0yXL/mb7X1sGAYhM1BxW4zG5zmMt57p9j2YkzXjZWZw3r49fw20uv4nS62PTifL64fRWZ9hD0VKEPbNr3KSpEwI6lumF1IwgCoTd1p9ivCIWowHTCSHHBb6irw/Bt1XDhj8by5d7piEhc277pJsTfWXNqDROXTmR/wX70Sj2Pd3+cNVevIcgQhE6p45OhnxDpGcmDGx8ksSjxT8vcnDTKBiBJkhOIFQTBBCwWBKEj8Dnwe6LzV4F3gVvO7icIwhigQJKkfYIgDL7AHDOBmeDejG3SVfzHCQgI4JprruHnn39m/vz5TJ48GcVFmh7+SpQq9ypXWVVx3sx+5xkAAJfdcXHzr/sVgLKQMyv1oBnT0bSKofDjT3DkF1CVv+GcPkI95eDAHdH56TcfItEJARjbM4xBGxIpMIjo9jgx62u/D72CF/ML6eJrpHew22ShUHqCC0J8ohqU1dPHl5Zhr6EquptBrhRGjHXb5+mfCsBVgNlioefPC4h19uHRsWd8y/Mzd5C8eQb9k7fQBchRa3EIWlSSBau2MwFTnyd3xgKclYHkvL6CCl04HmUWLAoRi70Mm62UQG1kndX2/pNFJN29Hkn0w9uVi0HtIvbKdue/6fUhSYCTqlJPFr/zK+MeGnPaDn823q3C8X4sHMklcXLpJszaFFSVbf90dGmltYRtxZn08g6mfVD9aSYuxJu732Tu0bkABOgC+HX8rxg150ahe2m8+HL4l9y46kbuWX8Ps0bNoqXXxZmdmpsmRdpIklQGxAMjJUnKlyTJKUmSC/gK6nWV7geMEwQhDfgJGCIIwtw/JfH/KW3atEGr1ZKcnExNI0wMl4rqyhqS9qSyY91hfnx/HZ88sIof39nAwe3HKc4u4HjxcY7kHGF+/Hxe//ptACRB4Mi6xU2eS+fhVvS2yqZfr8PhYNpDWoqNoHv7W5I2ub09RFHE95ZbiPplPvpevdB27Ij/Qw+e7lfy7bfYMjORXC5cNTXY7XbmfHAfW3+8hvamg/iqywAYnZhKgUFEYXehBGLtIp+G1xapEAU255WRVFTJV0cySLQdo9zphckYwfnIzXTbxf1N9XvG6LRaWtrbk8g+diYeAODTWz5h87uf0D95CwAH+91NwBPpiDVKrGYP1E/EAxD87ETstjywBWAss1ClUmATBZz2KjSijjxNJsmOeNZlz8ZLH4jK4yq8jX2JNhXTr3MNkz6ZyPhPpxJ5eb1OHedFEEVG39UWja6MnJOefPvoAixVVedpL2ANOYKkNmMKH9lgu8by0Y5HqHbB1A43X1T/t/e8zdyjc9EpdSybsIwN126oo+R/J0AfwMzhMxEEgTvX3Uledd6fkLz5aIzXjT9glySpTBAEHTAMeFMQhGBJknJrm10J1HlXkSTpaeDp2nEGA49JknRDM8n+f4UoioSGhpKbm3tOPpuitHxMwT715nlpLHa7g/LiSvyCzv9Knrg3mU2zkhEd7lWvhAgqgdKTAttOZoOUxSmv5Xjo3StZURCpdOUQk5lN2fO/kdO6DSHRnRstV9YpMyi80Po0PdPfvC2LqdY5OPjMnQx69kvSvviQDped8RLRxsQQOXvW6c+CUknxrNlYjx8nZfgId/Utl4v8q9SEDqvC4lTiqXAwEi0/pk+mrVWgWJTYfFknvM+69/edyEDSKXmvrJT3ymrL7qnuRiXdwvLqDLroG65uFdvnNnbtXEuh7Q1y0roTElU3K+WH495h8rLJPLLrQR6rfAq1KYGNYyIJOBpH92kLiNX7YUvcgUZXhdkSjFCbtVGSJPbZ/IgWXARPbEX7Pu6H0i8zPiEjYTXdx91LuCaf/b/k0a2VP8eyjfh6VXD5G+dPgtdYWsbF0CI2mhnv/caPMa1I/HULl8dF0Se6Bd7ac9+iJEkiI/1HVCaBtnFT/vTc1Vb399A1rOkmwO+TvmfOkTmIiHwx7AuivKIu2CfCM4Ivhn3BtDXTuHPdncweORuT1tTkuZuTxrxLBwOza10kRWC+JEnLBUH4XhCEWNymmzTgTgBBEEKAryVJah4fJpnTREdHk3LyJMfXr6d6+3ZUvyxgX5+uZAa3JMoYwrgpV+EV3LSNz/W/7OH4hnPzs7uUVlr388EU4IHRpOPkoWyyj1birNQgKBS0GeaBT6Anvn7e7FqcQWFGbX9BwM/ai8pOh+ndpjcjuo5Ao9Sw+pU78E1KZe8vnzL60Y9RqtTYrWbKi3PQevmQ+Fk8R486mfrp2HMDb2wSWkUpGu+me91sz3BviI3pexUHx+ygzdIEjiVtpm2HurnHy8rycQzrS6E1E21xFerVW1BY7NiDrDgvq8J53Juw4XPIzRhLgL4QzE4GGA28OqBuSoiFsTGsyi2l3GxnRXklPfU6evll8UZRAJcn2gkV1jDWZOGFzmPrBC55+YQTGfoMWcUPkZe1rV5FHxUQzkcDPuGBzffxUvJTxLa6gXXC5VQFtSTk5Gpadb4Bau33GlUeFosFm82Gwukk3yGR73Byb58QCtJzWfTGO1SXHEfrGU3PcYNxFYWgWriOfUmFKITvyXBdj+R0IpzHTJiYXU5GuYXBMb7o1Q2rE7tL4pM96XzZ2Qu7UuBnz2B+LrAi5B0hpqqYR0N9GdfVvRe1fskYNL75UNkFlabxgW0NoVWZEJBQK+o3yzWEJEm8t/89FIKCNwa8QdfAxlfnaufbjo+HfMxd6+7i3g338tWIr9CrGp8/qLmRA6b+RRxcuRTNI3/MDQMF14/lN6cBtaCkW3Rn+o2/DL1nwwmcTh07xcoPTqH2smArb9wvvyQ6CGivYvBVXfAP9mbJBwfIPl4GgMGkoU2vII45EqjZ4EXgeAcTR4043ffw1iU47n4KrR2yojwQFApCU9xRpFU62N3rUwCUDjMeQiURIRKZBSpKJR9aeeUx4s3rG3uLADiekcr9qx+kUJXN6qvWYDt5jKopd1DqqcDipcXmbSD2vZkEhbVh49sPE/zN6jpjmA0S5c/aERQq+o3cSYnZSsK+fmTZx/By/HC8gg0cenBwo2XamJPIirwsNlWI5EgBvBWSy41tRtVpl35iNyezrsNL+QjdB97b4HjlVZXM37qUmZmfEmnow9zkOaRd/RFB/gNRv9IFvb+dkhMG8vefydJ5pO1Ucv07YK34qvaIkojOwxn3yG2ni5sX7ltDcXICKxf/hkLTje4t+xHUPpKI8S0xZ9dQmVWJV4wJQ7ABm8NF240JmNUCJrOLUaKOCA8N9/aMRK12PxwsNgdfJ2TzeVEJxRqBFhUuvu8dzYbkI1TY7ZwoKiVe70ul3oO44mxeCkikyjETKrpz2bgfEJuhQMd3e5/hvaRl3NlmJPf1frvR/TYnzObeA+9wuW8X3h41+7wPvIbYkLGBR+IfoXdwbz4Z8sklTVEsZ6/8L2C30GLrW+T8/lkEbbCBwE6FtHXOJCbiRdYUaNmWso9d7x2kU1Brhl93BXqvMwo/p7iMcR+sIrdLJM9qZ2Erv/nM+IILpPN4RbiUFGdXYfLxZN+qdLKPl6H3UjN4ShtadHb7hp9afpQaoKLs3NwlnfqPp3JrP36bfh8tlh3CoYTU0Z1IE/NoubsQv6IEivw6Y1TWYLNAQr474Ve4Np+hrzQtAOVUXibT1t5Mtaqc+0KfINDLH6mrH2umDMB+4DCi00Xk4QL23z0VR7AvrTankROmwzW4F759B1GdlYZLFCnZswRPUx4V9khUGhOBGsgzR6Nw7UUUhlCeW01ySTWtfBpXX3dISEeGhHSkyFzGuN07eDrHn4nRTvTKc5VHeHR3jp/SU1KzCGhY0afmpzMrcyYWdSXD9Z5YBSc+v95Hhi2c5aZruC5rPYXleqr6a9EZYlHrvDEZl1BZVcDv1ezGP/4aMd3bnzOuf7fLUYZ2w/GbgZ3t2zEgD/JU91G42BNtZSQOTSkFyR6oq0PIkWIwd4qhf4XAYRXM01jBbuXjTYcIswiUKySKtAJOhUCoxcUrKhO3jIpCqVYQE3jG+6jEbGF6/DZ+9A3lWUchD9v0DB/9TbMoeYAb415mffpGvjq+ims63EWgMfrCnYDKbe+DHtYUH2LN3FjWjVlAkG/TirwMjRjKi31e5MXtL/Ls1md5Y+AbiELzJiFsDLKi/7ew8VW81Ifx+uUb6HRG+bmqK3G8O4CQjBlMGfg5haYRbI3fwsHcoyS9n0x7vxhatWtNSPsIZiyZiDWmmBGFsbQat5OMdRPReji48oEh6I0mFn/yMzmJdbMInp6r1MBXD9UW2RDg+hd7odG7VygpGRkUL9dSbSpk8si6tlCjlx9j3v4J6ytV2G1Wju+ezhc5R3nymA+VPu1Q2qu48s0RaHyMWKvtqNQKlJqm/XrW2MzcufxuLGINn3b9hv5d3IsbQRAY+fzM0+3i336UFt+sxHmyksxBren93Pt4hp/rHZE7djxH9o3GU3WS+OOfMrjNvSj0gwnka/q3rWTzURMj3t/MOzfEcXWboEbL6Kcz8UCwk4eylUzZtZLF/c6NVhUVIljbISkyGr7OGgv3broHh2jnlYh3ufKyEeR17o/j56msdnVlpnMMe9uEctekuYhCJdrylQQWWAlzScQEpJGg70rqSeooeYBj2xPZMKeAHV3i2NFWR+cW39BH7/b8qQ44iMJmxKmsAdGJKAnckj2HGTf0wep0YXO6WJyUx9LsUvJEJ6GSgt52JQNNnkzqF4JSXf+K2Een5b1RQ6na8ANLxViKwl9DrW56SuGGUChUeKg9EKhCJTau8MiJXZ/wlN55zjHjRWa/vKrVVZRaSvlg/weYtCae7vn0X5qnHmRF/+/g8ALY8Sl0v+UcJQ8gGozYY+9G2vc8mi23ETgtgcmP3kz6gZNs27CZQ4XHOVh0DM0WK118qtisg7vsuwisjGXAo5Fo/c7kDI/sEExO4rmujBq9Au9Q3f/Ye+vwKq7u/fszczwn7u5EIBAkuLtbSykVWmpUaUv1qbtQ6i4UCsXd3UPwBBKCJMTdPTl+5v3jAGlK8La/532+va+LCziz957Zc86s2Xute92LkvMNCDLwCXXCYpYI6+yByk5BZUMVB48mc2Z7GRrBhdHTY3Fzcb7iVFQae0RTHT/lb0eBhlp/m7Lg+IeC0bjb3AwahxurAgQ2f+qTy5+jWJHLf/zevWTkW8OAFz/jXL+hCAqRYZ2HtdrGxymSrMBX0Od9RHHh7xD5JG2D+lKZO4f+bvsY/+A7vLgkmecXniB/TBTLTxVRWaNHLRcxGCwYmkwoVHL6xXrzap8wQp2a/bO3hQ5mRflmEoxhrMqKZ5BvLC7q5jiETNAiKavRNdaj0V4eiE7OOoN/vSd+TQHkuJ4mrTCIyNDBpD2UxNwfdoMZztREE9V+BYqj7+BzagdWAfQqEXeDFQ/pKMXmTlitFkSxpfFtqLHFW4YVniUiMpHOdrtxrx9DSNcnUNm7oXJ0J/NsMjnFtyFZBWZ0qEMQBNRyGWq5jPs7B3B/54Ab+u4u4vO+44lPOM6CKtk1FOhvHBX6enxUCly1/tdsazQbObnlIGNkIznndJ6uvrCkMYPzObvpGHPXTZ3/wZgHqdZXM//MfFzULjwee3Xp5b8a/xr6/1ZUZcO+WZBzAGrzwacjDH2v1aaqcY9iDmqLuGYMinkdMPjch9/wZ7j7hQdpOJuCbvXbuBjjkVcZmV51cfztmE7tpGbEyzjH2XRN2vfuTlHGJvSNRqpy7THp7Gg7SE+vMZeLW53NzuTjV5ZiX+2OgIhcbofrcAPtQ6OuObW9J+dQJhOZHXEfmUdtn8mouYmb1Iytx/dy3HKAEeLt3DPk2kUforpfm7ZnrwlCDwiSEYCOYX34Pb0vwXa7EHSj+OH+Pjy19ARfrjlzqY9FqwABtM5q9I0mdsbnkZxTzbEnm4PASrmGx4KCSDgPT+Y6oMk9zVsBZqaF2zjefgG3U1i1n7MnltC5z/TLrsth/3w+WZVElTkTze96Xh1RTkenAs5UabBoRoAo4WlXzj0/VbBNtYd6rQL1E6ex03pRl7kCx98fZnqbY5w8m0jHds2s6KqiCo5tLEJhbyC4zxeEGRR06rgaD5+W3PnifV9ABHB8LI4z+lzzPl4v7JX2DNJWsaoxlAcPr+SLTsNxugKN8UYhF0Vk1utzmaw6sIo0IlBZILYqFrf6LJxdtZhMrcspXw8EQeD5uOepNlTz/cnvcVW5cmfUnTc93o3iX0P/3wirBb7uaPt35GjbSj7uAVBdeTsrj+2LsWoO0vFlKIsWIvy2AIvgjtZahRYJg8MILH0fQxnXD0vlGQzFCYjb38J540c0ucdiFzwShVLFmEdsBapL89NZ+UEBJzYqiO5RjIu7D/rGBhRqDTKZjO1rjuNQ7QkdywmP8qdn1644trL6/DOa6otZkLkGJ6wMjZtOSP5Wdu525NS203h1735Tt6ugqohfkn9BlMl4444r68rfKJr0toiIi09zMHhEzw84enQYjYWzGNFmEEefHcAD61Ooqjfw3vBo+vs3U1StVitdvounrKiBtKpGIv/gzx/i14XZ+v1YJAsLiup5JT+A1OplDHV3pqpkL+5KAUfXEMCm8ogACXs2sGXFK4xMlJDVqdBgy3L1LhbIavBiVGg8d4fFU691wMuuAoBDOAMQeOhB2gzZhGPYHRTVz8TXoZ6yeW/DpzaJqrzT2Wz4JgWFvYGQ4e8gmdV06bQCN+/L/dmCVwHqmlCCaidy/OfT9Hk6Dpn8r0ng+7TzWNQnN7KoPhyPM/HM6vTXkPfs5HZkNV6Zuw+272vDkQ2cPHwSuSjn8Semc3bVHKQVxXxX0oC2dzBpp+M5v+gnnNu0pfe0G3PBCILAO73eoc5QxwdHPsBJ7cSI4FvPE7ge/Gvo/xshykDpAMZ6iLntMnfNlaAceAcMvANzdhrmzV+hqNiM0XsK4ojnUIc0UwHlnjHIPWOo11dh3fox6vlTqPHxRzn2J+x8bCs0V88AvKITKD0bxLJ3j+MW3Eh5hgueEaWMe/wO5Ok2X35wx60M7zHvmtembyhj7/FvmJu1njTRwvuhk5Ep1EROnkD+2blkFXtRl5WBY2j4NccyGc2U1VWQmVXAd8nfckaZCAqY7DgNR7u/ZgUIkF26Ey/A17mZ+683WVDL9ajlenSGRrxcHFk7ufXaAKIoYq+SU22ReGNfOlNifBkf5oFMFBEEganhtp3SbYG13HtsDwsbIlnYAFM3bWHyGUh2/4KckLdRHhRw0Em41VdxL3DeB8Le+wXCA6gcM4qxWQnMGDCTSJ9MfD3KMRhcyNFNIMS6EUlrc8VJjXqMZ85gTEpEpnqcEuNJDP5lAJzak8y+pcW4RR3Evd16ANztX2jVyFstFtCAUSjmVE8X2h+qZutPSYx6Iu4v8Ttr5ApebdudRUfKWFHjwsSyc/TwvPYu8Wo4V3qQpJpSujhdPU9k1pJZGM4bEOUinQZ2ws8tkPzjJTgX2CQtzvz4FV6HMgnTSUAiG9esIei5l+kw4PoJA3JRzuz+s3l0x6O8Ev8KjgpHevn1upXpXRf+pVf+tyJ9Oyy+A2ImwaRf/7bT1J+Zj8PypwGo8fXDefqZFsdzzp1g8zclSJZmv7lcU4dZ54i9XyKpMXO4q+t3dPa/ss53ftkpHtx0NyUiuFkl3o5+iAE9Zl46XpeVwZLZ5wlwLWbke9MQWimMYTSZSDh9jL2Z+9lXsZtKtS1XT2lRM1w7ngEh/RgS1+cvK6tosZrYsCsaB5lEXPd9OF3w7VbUVpGc2BUA0f1HBnYYetVxZh3K4od1zcKtgr2CAB97Rrf1YloHf7y0zfe1qKmen1avYsr7s1qMoXNXYJLcUJpNZL17L2OGPIBKZuu3d8FavD58hUTPCH6ImYCLyodAs8iUQQ30m3gbu3bbjLXnmftwKRgEgB4TS1UJaEMkxne8my0/5uEauRXP2FWY6tvQtcfPuHldnsVrtVg4s+c1SsUVqHUh9Bi2heI3DmMWwPGNbrja3XhspTVIksTziWtZVheAp1jNtm6d8LS7fhG1P+OL/Q8zN/sIcwd/Rlf/1mMyAO/89g5SjsTjzz6Ol7MXdQWFFA6x/a4r3BS4V5qo9FAR+tMcTq+Zh9PqvSiNVupfeoC+U29MeK3OWMeDWx8krz6POcPm0MHj+hMJr4Sr0Sv/NfT/rSg7B78MAlMTPHkEPG6M1nW9qDn6Ec6bPwagzj8EIe4R7DtMRxCb+b7FuelYTCZyz2VwcqMDSocqvCP0qAI/IEFvJdPqwcKJl2uGV5WeYuORz5lfcQwDErPaPUKPuKeQyS7fSB6YPY/kzCAmP+KIR5eWv9Wk9FTe2PsWeap0AHxNwfRw6IfWUcWD/e7BXXtzJeKuhvi0HzAWfkqlPJw7+m69tFqVJImF21/EV2GTdNBbnLDzeIHeMZNQyltXsKzXm0gqrmNjRhm7UkuoqtKByfbc2bmpCfVz5O72vkxp50NaW1vmrP+x4+RkFmKnO05Yz7uRJKnVFbMkSWz74Fvcl86l1H8IOcHN3Py73vDlxNlxKExudGp6HplCjiI8lHXfbiXVswnBKsejvAMWcx1t73oLgAH9z7cqmFZfep5Tx59Cp8nAtXEosaO+o7i0EenrZE7FODLy3tjL+twqNuYd4eFMFVMdspgdd9tNjbHh9Ne8evwXXOQiW+44iFZ5OR1WkiR+WvUTRalFGNQGPnrpI0RRpLG8nLy+/ajs0J4Oc74n6+gOonuNRWVnc6GW5qeR8tDd+Oc1kTUyhmGzfkehvP6krApdBVM3T6XeVM+CEQsIdb41XZx/Df3/X7HzbTjwBTx5DDwi/pZTSBYz9fteQDy9HvvKSgCMCgHdiDdw6nK5v7uuqgw7RxdEUWDGnpfZymiUpbOZ3/MNugX1AFGkujKDNUc/45vSeMyCQAdUvN55JtHtr5zOXpV6iiXf2tQq7eVVREUZOBumYmvRJs6JyQjAROe7eKDnvQR4+/wt9+IizOZG9u23rbBU/i/RJ+LRy9rsTl6HVPncpf+X67yZPDK+1R1F6vlKftuegZ+TBhdnFUYR5icXUljbUn5ZdFXx+8rXca2rxXrvTNq9fnkg9ko48dsWDh5W4WrMwLGPPTlHvek+5gS1dt8TLfsW3/4tk7PefvttFAZnHMockazLiZl6HmhZ/eoiqjKPcSJ3CgCh6lcptxtBdXwhjo0W/GrM6Ka3JTL0r3/ZAsTs3UegrJLNfW/O0M/YPIqDFXnsuH0LrtrW2UBbjm7hyOYjNLg2MH3ydCK8bc9a6m+/Ift4FsKzzxD1WOuqlwZdAzuev5uw3efJGhvL6NlLb+j68uvyGbVmFL5aXzbfthmZePOxjn8Tpv7/isw94N3+bzPyAIJMjuOgL2HQl5jqc6g9+iEuCctx2vAuup0fIzy0C7V787bS0dUTgCpdFRuEyRgFFWjieGj/o8QYzZjlKs6JNv5xP0nNzIGfER7Uekm6P8I1pj1jbttB+fkSivINfKw/QEZZIhrJnghre94c+goxgbfmq71elJY3F4/oGd56ebhBseOB8ZzNT6Xo/Hjc1KVM/2I/8WWNuMvkrHquL97udhw6UcLUpYmYBeBP9baVEhgFUCFgEiQ8ndSsfOtnJn/6Jtrlv2B95SHE68zG1OfXAp6MeX8S6+ZuAsDZ2ZVaI+jk2Ze1f2Laoyx7/X2MplwE0QtDiYjSM42m+krsHJqNdlFJHjuPz8LPA/zlD+HbbRqFsw7Trt4mc3wq1I6Rf5ORB4hRVnPScHP1DADO1BThr9Zc0chXNlZycOtBZMh4c/qbOKr/KLdh20HJnJyvOL5KY8+Y79ezaepgvLcnY3i3AZXm+nMAzlWfAyDSNfJvTaT619D/t8LQAMUnodeMf+yUCodg3Af/jLnr6/B5ezQ6I026y2sDbCgp5JGz5SDYfLLdzG50ranjjHsQOlMjzzq1w9M5lOFdn0GpvX7fatCwoQQNgwU7ZpJRlMhQqSfv3/cVdspb1zu5EXh7jePc2RdosgpUN+bgfpVMygjfKIrOQ1pVG3aWN+KHjEKrmR6f7sEZkRqsqBFY+1B3quuNGPRmrBYJOzs5PTp6k55bS5ivAyp186OYvD0OxbY06rKKcW5zbd43gMFsMxJ2bvb4R9lTWwCp2Rtx8AOtc8hl7WXHTmE02YqXdLvPBYPK5norK0giOHooCeeO8fKqcxTUuwIP8GLwJHpNu51NvyXTrd5KZh9P8LCjb+z1J4vdDIoMIq6WOjIydhIY2Aul8sb0YgTAVW7P6bzTIMCepD1UlFXQWN8IRsACWqsWVYTqT0YezCXFyACVl+c1z2M/ZAh2xxaQnRxPVI/LpS1aQ0p5Cq/Ev0KsRyyf9Pvkb02i+tfQ/7eixFYJCaerS9v+HZBpPDDJRcwqJRq/gZcdP1maw/M5KzjtEMxRr4Gk2UezpLqGs7EvEj3gqVs6tyRJzC/YiR8Cn973PWIr/vy/GzJRRommP966fexIfpm7+qy8Ytv9m/8DWjhfE0o7tZoNbw5k0ZYMPo/PRI1ArQRvDmhDTJvWV71tQy9ngvjdOYaybYspHDucrHa9qJ8ymojuXfAJbLkqtVqt7Nl/iDaR4Sg0tpjKF+8fQKhyRA1YzCoUjRF4txvZok/hU6/RsHstkR7OpPm6oRf2ctHEZKb/Qm3VWZ7e6U6lzpUpHSpYmuLOmRofdi5OpVtGI1mxLvQbHfG3Z3caLRayLL4EVJWx8MABvL13MH36B9ctjXAkeSedM4eitmhYkbLi0ucm0YTcTo5MI8MsmXELc2PG2MsXVI6xsRiA0jfeJHDQoKuey7t9V6wsoDT1+HUZ+sKGQmbsnoGHxoOvB32NWn5jgms3in8N/X8r3NqA0h62vAi1eTDs/X/s1IJCQ0OHYbgkbcX8gSvSc2dRaG1+8dpjH/H6po8vGYYvi9L4uO10vg2Ywuik3+AWDX1m1k7KRJjh2vn/iZG/iKyqFLw1YDUWYbGYsZiNKFUtV5NGYxNm9UasdXE8MuAdokJdEUWRqaMjmDo6AqvVSmmVDh/369PDuQj3Xh1p/Oh7ir/8AqfTB9C8cYBilYqc2Z8T0iaUgxu2onBzxbJlC5HHD9MISK5tocOTaMqhzl6GQ0AGewICWCl7id+yyhkR6oG5poHs26diLjyHMSoOS6ATBouRk4tE3EIr8etZhswhkTJ9ErWGT5EJVpam2HZkfSPciTlWxRkXGcPuivmrbnOrkCSJzecy+DS7GLPGkW6WCtzcmigpsWPbtu/o3n0Crq5BrfY1mHRsSfuRxJIjrCs4gxQoMbp2JAFBQdTV1dGvcz+6R3a/rrrLoSNHcmDRYtyOHyd77Voc2rfHLTS01RdccNuenJNB09nT1xy3zljHEzufwGQ18d2Q73BV37xr6nrxr6H/b4W9BzwWD6unw5GfIWoMBPa4dr+/CA6Dv4KkSOQWCUNDAWh9qEt4C6cdXyIBVZ1HQPJhHqlcxRd7xvBR1H20E7O43ElwY1iZ9A0Afdrfd8tzuBWM6/gBpeeewFsoZe8+G+NJauxBhy5v4ukbiUFXz+HdnyBqTHj7TCKmzeUuKlEUb9jIX0TQxAEcyMqi8y+zOTFsHN6nEnF9fiaVZhPRFwgURrmcnNjOlEoCSg9vTIYGjmlEfv9kFKI4gOgth0EG03ILOSFYaJz+JKbCcyR26UJmeChcMFhGlYi6RIdMafO7Z9cGYbY2s67eQYN9vQmFBGb3v8+NJkkS3yWlMq+0hkKNAwqlHdPkJj6+6z4KCo4zZ85Gjhyp5sgRW96Gr5+FCeOn4+lp2+lsPvMN7yX+TMPFKokX7HGpRzkfj7+5ouAdPp1N1shR8J9X0AN5M56i85OXi82p1FpKguxxij9FfkYSAeGtSxqbLCae2/McefV5/Dz053+sAtW/hv6/Ga6hcNdSmB0G+z6Bqav/sVPLtd7U3fkNjstm0JT0HaqRv2EutukV1E/4AFXI3UiJbUmytkGwgOJ0DQYXha1s3C1s6Rc12QKHbYOvvlX+K9Gkr6Ws/DRlVemU1mRT2lBIaVMZGUY31Oo6QpVWIiw9sNMc5+Sp21Alj0VvSUBuV4S5yYuoPuP+lusyDBjKwE6d6FMv8u60u8l59TWcaqrRfvoZdho17gF+xHraEtdGLznG6eQyPrm/8yXmT6idA4lWExq9juy77qDJTsnZIYOpdHennSjSbcgQ5qZsRcAbh6C0S+c9WNSyWNxhxyReO9sdowDeFwqW/FVoNBo5nF9EjdHE3LwyEpVaXEU5j6mtPN0xBleNzaXh7x/HAw9ARUU++QXppKeVU1Roz6pVv9B3wkA+P/o+x2rK8FUpeDh0KLfFPIuzxptnVj3DnsY97DyzkyFtr5zrcSU4ensTtnEDp999D6d9+9B88y1Nd9+NncvlLregN96h8bHnyZ16P8L8BfhHdGpx3Gw18+qBVzlScoT3e79PV++uN3fTbgL/Gvr/YkiSRP62X/GxiijCB//j53eImkqd+9toT6zH3K8YsaYQAHPxUdLy5HQVdLiNeRvHPQJ1NQZCGnMpei+KkqBxhA1+CCf/m2fJrNn+HBOHfX5L1y9JEkW1NZw5exS1mEZ5XR4ljUWU6iooNdVRatFRhoU68fIXk4PViqckYih2JtQyBM+gfoR3f5vEwzMxO61AavTBVfkubXuOR6H8axKF/ox7Y33ZsKGSA84SRq8QRm3d2OL46YoGZm0/Q0pBDdnnqwkKdWZydDP19D/R/txxOpv2GWmUe7hwoktntI1N9HByYvizzyIIAm/26M6rn71CkSkEX2seogilTS0VTI8bPOk72B6zAIPMjfxssaJqhWt/vahp0jHnVBqZOgNbTQK6i/kHSi1TpCZmD+mGohXXSlBQHEFBcXS5kIi8fv27xKc0smjHM+iwMto7jDcHLsBO2azB/86Id4hfEc+y08tuytADOPj5ET37LYq62RYf+Rs3Ejl16mXtonqO4tS3BqQZr3H+qen4bz926ZjFauHVA6+yNWcrM7vMZHz4+Ju6lpvFv4b+vxRGvY61H79D/tlUgrXRjAodwz/LPbFpcwiD3kK5/GmYHYUzUNW2Oy7Df0W/9FMAXH2CGRupZ9GRPD7XzOAx61I6Zv2CmP0zp7S98Jz8BV5B12/wj0zazUPLh/FO0XZU+99mVL+3W21nsljJq67lbHkeGZUF5NUVUdJYQoW+jDpTOTqpCotQjSDTt5yTJOFuBU9BTqDMjjilE14aD7zsffFyCsbTNRxP97bYOfiCIHBuyTpWiqv4QPcpLsstPH64PY6PvE67MX9Nyv/VoJTLCLBTcggjjn+S+E2tamDMV/FgsoJcwMPPnl/v6NiiTV9PJ845teOLBj2F/n441tby9AcfILdrjjXIRBkBMgMVdYHMLtXQzd7EfW2XkV0biNXsiuVgGIe8jeTLOyAA2yrruDsli1Wdri1V0Rp+O5nKu2VNNCmUgIo2hhomu6gxiiLd3V3oE+h33WMNHTqD2aVjqZUkXmx/F/d1fu2yNi72LkQIESQ2JFJeW46H05VluFtDU1MlJ0/Opa5+Pp6dLWiSZFjmfE6pj5W64/ux6xyHXKal8fwJvG9/ivZ9J7J+wjraLD1C6t7VxAyw8f/nps5lS/YWnu38LA/GPHhD1/BX4N+Eqf9SbPzqE9IPH8DRQUVtrY4wPw0TPl9x7Y5/MSRJwvShG0qThap2PXC5fQsmo4GiWXFo0OP8n1SeW3WWTSnF/P5QN/q28SAjI438XT/Ts2g+OYowol47fEPunPrqbKavnUg+Jj7s+Ssrz5+ipLGESkM59aZy9FIVVlk1gkx3WV/B4oBKcMVe5o6r2gNZTgbThMOUNbZl2PRZeLhHo2glO/JqCHl/C/YBL/HMWgtazymoPPzpOqQDAQNuvFD2jeLdbSl8r7RyDyKfDWzOZxi7PJFTSSW8d3dH7onxuar0g9ls4YO338bZquCZD9+89LnFbGL9D2+TXKnAW1OMa/BZPFwLECUI1gygIupVRqc2C4F1VeSRaQ2gyiJwm5cz/VwciLZXE+tw9ftptVo5XljCnPQc1gsavAxNvB7kQVdvT4JcnG76hWm1Wuk8vzO+cm82T728SthFrE1ayxun3uCF8Be4v3dLAeSEbVs4euwoLg4OKDV21Dc2UF1WhtKhioDwNFzcihEECb3eGze7iTgeS0b4tXXbJCmAHn6IHbtQO28j2kYrpV4KZK89w/Ml3zMgYACfDfjspuZ6Pfg3Yer/ZyjNyiD90AG6jJlA/3sfJOH9+zl8qpLsbQsIGf7PBikFQcA06XsM+hpcOzwKgkBjwWmCpQL2R75GP5WGM0W1CEC3YBt7IDw8kvDwzzi03JueZ97n2Jbf6Drqges+p4NLCBPdu/Fe1SGePPLIpc9FqxaV3BUPuReuqvZ4a70JcPQhzMWfKPcAwt38UMlbulE2vX4Xo+RNzJF88PHpclNG5b1BIdQmjGfUs77Myz1FIo6oyg8SQBx5mcXs33mIgCA/+o+4OfXNP8No0JO4bz41TSvp6ZDNXuktNhLIRRNR2GjgVGoZXn72TO1w7RXwnhPnkOQy7Du3rJe769d3Sa5UEOehZ8TDX5G5bywFMoGOni/gEvM4QcC6dueYfLoGLTqSTL50U+RyQgphdWkNq0trAHgu2Ivng7woMZpxkIloZTbRtqNllfxyJoN4o0SdUgOiHZ31dSwf3BN75a2X1DOajVgFK+F2V08oDHYNBqDxDzLDO9asJOnECXSCHASB2uo6qLaV3hLUarr22GPrUxNARNsXiIgYadPuHwBp9VOwLk8GwP7N+0AuoPYNp3zFL0gH8pDiC7F3lVHQJZzAfRkw41Pui1Uy/udnb3nON4t/Df1/GU7v28We337G3s2NzqNsQb5O973I8ZdeZPXc5dxhqidwzJVLzP0d0EZOafF/i8bZ9g9dDQDVTSbUChkqRUv3QtzEZ8hOX0S7Iy+T2FSJytWf+owEVJ5tcEydT65TV3o9/CUaTTOHuCQ/g4yDaxl2bjWpbg6k2ffn6UEz6ewbgUZ+486rqIdnkTvvOA+wke9ffZInP/r+hse4t1cU9LLRWwMOLKVy5QIqamLZlfELx4wN6KVaMopT0DrYEde7/Q2PX12WR2lBBkZzDiUlq0FZgExZj0zpj2icTInKh1BZ5aX2r+5JA6OVFwZfX8b03vhDIMmYMKA5+FdblMHhYujkqmPMkzYRtaC4LyhMGkdpwTJcYmyFMbp7RnHYvhpHhYp7jmwj36zlZO92LCmuIqGmgZ2VdXyeU8qXOaVYWzm3YFUSbWlgoFrF2BB/Yr1i/zKXl1qpxsXiwvnG81dtp7lQYDy7OpvMkkxCPEM4fiIZg6jA007N/U88idlopLq8jH3bt9Glpw+lF8otjrtt72XjtXnjd5LVd6CznsMUE0JMzGREUcSjzyQs+gZyf3sLw5ebcUopotjRBZ+6atQ6DbmlaoKdLhvuH8G/hv5PyD+dQu6pk3iHR+IXGY3GwfHanf4CNNZUc3L7Zg6vWoKrrz8TXnoDB1cbZc8uMIbbn3iEZd/NZcvSDTz6Dxv6P6MsZRfugPsFCpmrnZKsikZqmow42zULeykUSuwfXEfOb/fQJfUPRVMuFL4Nr8zg3KeJ1AcPRzI2YazMpXNjPH0EAwCd6nrx7lO/3dK1hgUHstRxIMPrNjFCsYuGqlrsXW/+aZva+06Stiyhu3oQSw0JAPg5taGoJoudO3ZeMvSlBZWIMhEPHxs7o6ayiMLs40joqKnMpNFwBOQFICmQa5qzj2X2YGnogLfnZNp2mkJObT5VJ6oYqSzDbLHyxZEc9h0pxNHDjknR185KLa2uR6gtQnQPws3R5mKRrFb2b1iChEC/UXdcaqt2jcHbGkShMo/QmnSUzrYXiY+dbQ5tNAKH6zwZePQsL4b48nv7EMYmnedEXRMeSjlt7TVYJAmDVaLBZCa1ycAIYx3zRg246ft9LchFOTXmmqu2CfMMw8nixNa6rWzdthV/qz8d5R1RWWU88dJ/LrVzcnUjODLalv+w91UAjm7/hK5Dn6eqOgd7rRdNTZUcOjQNVQ9bqUddxWts2TqP2A4fU1JyEoulmqCJ0zCd34L34Rp01bbnoVTqRF12Jf3auP/jZQThX0PfAmmH4tn41Sc2iiAgVygZ+MB0Ogz+e4oDFKadpSw7g8zEo+SdSkaSrAR37ML4F15Hrmi5tfXvdxt+i+YAf02Bh1tBQ5mNAqkz2LTOPR3VrRp6AA/fILRPrObY/Jk0uren+8SnSZk3A0VTKWLUSDwTvyAq8xsskkC16EKuU1eerxxLntWDHc9eXyr5tTDl+Z9Z8vpt3CXfxde7FvL0HTf/ohQEgS8/WMfm2R/DBU+Ah5c7hbXn0VPLnk2HSTuXTkldNgIyJo/vRmNDDrXmP0gPK0AyBKEwd8ZMPkJjTzw8+6CQe+LuG4i7jy0ZqLIyg/iEJ8FhFov0banZdopd+23a6F9PvvbKuKa+kW9+nocMK4P7dsdq1LF/4Sccy2+iUdLQ0b4Kl7CWWvruPqMpLv+B8vQ5+HVryT13vcAuKjZaeC4tnwaLhY1dWt9V5FdW0TUlDx+nv64+QGtQiAoUlqu7geQyOWsmrWFd8jqyqrPYUL0BuaaYadm38f3HH6Azmgjw82XSAw8jiqLtT0MfrPYHqJf/xLZt81AojZhMGkBCrjBhtdxOWNgICgv3YLEsIy29WZNeXP4RkQ5GGkdpUdZWsbukN2tc+1G9J5NlxwroFuJCQkYlzwxuw4N9bjXz5Prwr6G/gPPHDrHp69n4RUYz5tn/UJ6TxeHVy9j16w+4+vjj3/avzQZcM+sdspKa6VfdJ95JZK++eAQGX7GPXC5iMLa2Qf5n4Ze1HATokvAoZ1y86R7iw8mMSj5Yncov0y9P6rJz9qTrM4su/b/7k836+tbRT3DiZArVRpH+3WL4ZuMZzpTlMKmLPz7ON6ZrcjW4TnoF1u7CL3Ur3IKhv4jAWD+OHszES6yhuKj40uf7jm0FScROW014+BGKamzJPVazEmf1w3h49sPVMwBH1yuvxhMS3qeu/jRK5VFc7NS4lxbScBJ2XTg+qn8wAwKunk15NPU861YtR2Y1YxfVm24BDiz57CXOG9wItbPSPsKTmBEvXhYkd498DEXRD1RU7uLP3v9dNRIBFDK3S39GJWXyZkYRibVNjPJ0YrxnS175vsJSANq5/H2+itzKXAopZITTtRdiHo4ePNz3YawWCxsWbsC1zkRA3XLqvMIpy+3Amfwikg7sp0vf/lSVlmKVxpJ0WIu9XRVOrsXozG44O5UiiFbcZeMI8+mAp293QkMHUVg4jrNnl+Du0Q1HBz88948BwHL3BooWTMUrWs+WJyayMaWI7/dmsvlUCQCJedU8eMsphteH//OG3mI2k3Yonu0/fY13aBsmvvw2Kjs77F1c8Y2M5vf/PMPOX7/n/k+/u+EtlyRJNNXWYOfk3KJvaXYm2SeTLv3fOyyCPlMu5+X+GSISFQ2Qs+EHgkY/2mqBjr8a5vo64r9ZRU2tAp1eht6kpq3rYOxIILlxLHbL5lKvfIjH6tTIkhr5/JMjDBwVQqeYawtB5RSUsXrxCiyJWxGRONx5FIvrgpGLAh9NvHFf99UwtEMcJ1eFEUsm9QYDDqpb477HDJvKxoOvUGp1JtrFldKGHPxdIujRJ47AMF8OHu6DQlmPuTEAT5dHiO5xG2q7y2MMNTUFpKT8hJ1dIJ6esZjNNegN81Be2BiZ6u2ZKC3jd5rri3bV5gPtrnhtyem5bF65CAXQrt9IQppS+fibBARcGNMlgLixD12xr6i0R21RYLlQJ/cisutLSDX78qDDGdo72vNlVABPns1jXXkN68preD29kJO92iK78Jtckl+KUqllTPD10yVvFBuTbXkFQyOvXvwFoKGmkpcWTOGIXQkooFuahKbYQmDQKVzdCjh7pj+bdu5i046dSJekNzxpNHriWKPE1cmDlLwYJETAwG6O4bZlD9OfeRk/v674+dniH1lZWaxiJLezhYb809QoPPCznict4wQP9+3JgYwK9qbZXHX9I26M6nkr+D9t6AvOprL1hy+pLS3BPTCYCS+/ieoPHGOVnZa40RPZNfcHaktLcL6GDrrFbEaUyRAEgeriQjZ+9Qll2Zm4+vqDIOAf1Q5HD08OrVyM1tmZEY/PZOWHb9BYU31d19vtjvvJ/mEBqxZuYkSTgXZ3Pnsr078iJKuV4kOHqUjPI+dsI/l1IXio83C2N5Jd6UVixV0kcidge6i1golGDxWCyYpTViMJ357iaFc3esapSEvPpHuPLgQGtLx3u/Yf48iPH6OyGGj0jECoKsa7dAWh2nFMGjoehfyvfYmJokCqzIN7OczORcsY8uCts5cCtCbON6qY/MAYqiv64+zugCiKSJKEQlkPgKD2xaQsJyNrE4GBfXB0tK3k9fpGDh36ELNlGaIoUVsHtXXNY0dHbUGptOfkyQ8Y4LSVcfcM51iRgp8OSixI0PFAv9auyAa9uXnXdzp+C6cBH3kdvXt2JWbwXdecl2g2IylauuCW5yQCfvR2t6lp3u7tikIUKdYbeSuziHKT+ZL+UU51LYl2znQwNeGk+fuyP06WnEQuyRnc9trJhF8vfop4pxL86hS4N2ropC5HMIo4Os7EYv6Ort3WciJpJA0N7jirFHTqGEuP/oPY+dMrHKsNJaiugKlsxXLBdVqJK1utA5j79UdMmfYYLgE2F9batWtR4Y4ZGW67n+On0Ef4KeAr+qcf5vkjJkrz6hnQzovnB7Yhxu+fif/B/2FDn30ykTWz3sHJw4vxL75BWOeura6QvcNstVbXf/YB3SZOJqpXP3QN9RxZvYy80yk4eXhSlH4OQ1MjFpMJhVpDZM8+NFRVUpadSUjHLpRmZ6JUa0jZZeP6eoWGM/7F123BVknC0NRIcUY6PuHN/k5jUxOHVi+luqQIraMzoV26EdRrPF2TjnLsyDkkff1ffk9qK3Us+fAg6MxYrArAE7mgp1/fBtrfMw0AQ1U5x+ZuBqUdkRP6kXS2hD59otFqlUhWidNnKtiw6CyOx6rYvW8PFsMJMlcrcO45irqaGgy11Rga63CqzsWi0NL58f/Qv3cXVs57FbeQo7zEt9SLFiTpOYS/WJ9b4TuKusIUArNnkVfZi0C3m0v6uYgqnYQGHYIo4ur5RxeFBLTFYknHak2mvuEI9Q2Ql+/LqJHx1NeXcvDQAORyI6IISsU9mC0GTKYyJMmCUuGDj08bqqrOYbHuwWzwpm/kbXRtr+FkwW8czr260qGTtqXLy1usZlz/Pihcri5fUHXmEEfX/wdFNwkXswsVJaeQyzU4uYXRztEDKuClbAvRrkWEOvoyztMZgGUlVZxp1OO7LwWFIPC0p02PPUXx17neWkO5pRylVXnNYh1pyXtYrk5lSI0fXzxjewaTR0Zi9pDoGvcUhw6V0KRbQscO3vi6j6Bt52Yq+sgnP8Z+2VfsyfRnnsdUXrjrXs6n7Sdw3ztM0m9ipWU0q+Z8wf1vfMOhw/HU1dUB7vzEPYxmJ/N9JwAQ7xqHPKsKSSOSFaIh0tfhHw3K/p9LmLKYzaTu2U78kvloHByZ+vFXKDVX/kFaLRaWvv0yxennEASRwPaxlOdmo6urw9nHl5qSInzaROEbEYVcqaSxuooz+3djMZuxc3Lm8Z8XXhqrvqoCfX097oHBl77kb6ZNxqhrAqBt/8GMfGIm+WdOsfzdVy8Fhf8MpWjmqQXrERStl667XhiPH0H0cEMeFE5NUx1v79pJm03OAHSOyKHdbYOw8/FDrroxzrPJZOGHx35HlDnjNtKenOXfobSasCCiV9qDUoMqoA0PP/M4bi5O5KXv4lzmEwgyiUqTDx6aAjQe79Cr/b23NL/W8PuzU5jqvIUvFF2Z+drOWxpr45fPcrzGGYBQrZ5hU5+lsLCQpKQkfH198ff3JyamHZmZ2zif8SFyeRXDh53lxIkFVFW/g9ncj2FD5172wOv1TRw+/DlG0+9IVpHQ0G9o08aWvj/pm3kcL/Tk2Ctd8XBq3T1WVFnHz9/Y5CPGsw2N2oOtJ81YJJHJD4zHoNODIOLfZwJ7Zz9LWUU99nZKcsvNeHSswqdbyxoEjTUh1EQ9x2m9kfm1wYzVZPBLj+bgY6PZwuTkTBLrmi67ll91zzJs6BYUir92ZZ9fmc+YDWPwwIOd0678PUqSxP1vxZDmJ7Bu9Gq8/S9Uj+rTDuwVxGw9ScLBN9DrFxMVuRU/vzatjvPSl99gEkW+eNoW36moKSFvyzusT/NCrxaZ320MkiAy5egOHHVNHPENJrnNH8orWiWUh8oQG8y8o/iN+2Xb4YGtENTzL7sn/yZMXUBFXg7rv/iY6qIC/NvGMPSRp65q5AFEmYwp78zCqNOxe+6PVBbm4xkSRs/b78I3IgqL2YRM3tIQ9rhtCsk7t+AR1DLQ4uDqfokyeRHTf/iNtbPeoeDsac7s28XIJ2ZydN1KkCRiBg4lpGMXjHo9qXt2YDYa0JdmUdsopyYjEZfom/uRWGsrqVu4kYb8UFb6V/BxuwvZj/ah3OtZR2CFmZ7P3XyatkIhwyfMhdJcLXeOH8TpmAga6hqIbReGnbqlbzzlyFcUV32HZJHRIWIeWt9OHE6IIbfk0N9i6O36hEMqaPG65bFCo2M5fshWvCOrUc2PP/546VhhYSHHjh1j/fr1qNVqAgJUeHgaMZsbKCpeh1oNkZHTWhj58vI0kpI+QCIRhUKPxRxCTMxnBAQ0G4yR7Tw4Xgi/7NnNS2MnIW9FyrlGV4VekmEnGHGXysnM1WGR/JGLVpbPW9fccMkWADzsBWrrjbQNdqT3PZ9TUnOWsuIDSBYJvSGfxc7d2FvavBsI17R8MWnlMjZ1iSCjSc+y4ip+zC/DJEGE9QxqdT4JCW8yYMDsW7vZf0JBdQFWwUov515XbZd0eC0nwkR6nrFeMvIAqvE9Mf+aQOGGb5B52NQvk1Nm4OfXeoatwsMTRVYzX9/d2Rv3u35g98sT+bH/W5c+92mQOCj3b2HkpzSVs6NYQ2ODjaUWoygAK5iXT0P+YrOY3N+J/zOGvqa0hJUfvAGCwPgXXiesS7frDmaKogy11p5RM1647NifjTyAo4cnfe+6/7LPW4NKY0fb/oMpuKBjnbRlPUXptvJivSbfc+nFEDPAtqIrPbaFJZ99w9y3P0Ats+DhJKPrmAmEjG695N1FSGYzhoR4DGfyaMzzwCqFItDEx+1sBm+C8Twzgv3Z1c4V2Z4yvlp+mmcmXzngdy3UVxkRkCGKIu3bNBdPOZe4nMqKQ4RETOZs8tfgeBSrzom4HnPx8O3I4bNrkYtWuIzz8dfA2dlWLapNU+0tjxU99H6eCDpGyfkkVifaWCbR0dEMGTKElJQUMjMzKS0tpbGxkbp6dzw8c0lJeRRRtNVldXFuNjxFRSmknLobmcyA2dQBH+8JREffjSC0dEtM6tGPxcdW8PNhbzamLuKhXl6YzPWcKqwmq0KipMGRaoMj0In3en1AuqUH5sIOEL+fPnfcRV3Kdnxje5J/fB/JGTZ+6B2fLkTj0lwYJTwgivD2EwHbivi9PYsBsKceE0rGB16+aDyVW8Unpwt4METNPjGX8xYP1vQcxpF9y1GpVrNjp5WYdg/h49P2lu87wBcHv0AmyZjUZdJV2zXpbC7OUb2mtfg8+PFPSV/Ri+of5xG37jD79s9CpTrP5i3T6NH9A1xdW/7+7LRaTBYz844k8kD3ZkrqsoBnUUoGjIKKF5reZq1qFCV17ohlOqyeGuaF+pOYZ0/DgTRkdnISXxpEXXEbmN+VMlUQf60W6JXxP2/ojbomtnz3OTnJJ5ArFNz5zizcA1ovWvD/CodWNhcU3vPbzwAEtGt/2eofwKvrSO5/14PM7UspyUwnt8zI6gVrGFGWS7sH3mvRVrJasRTkYsk8Q+2eKozGYCAYtWMBDgMcUXYfwt27zrJYYebH4bbEmdAQM18lVmJKrYbJNzeftEPn0DW4oXFo6QI4e2wRBdVvISokzmavB0egrhNDRi9AobKjvKaI3OwvQXJj9KCZN3fya13b4a0MBrJMGi6vnXVjEEQRz6jueEZ1Z2vSK5gkkTvvtLFjBg4cyMCBtjNIksS27TYDV11z+BKjRqOx+fVNJgNJJ55CobAQFbmIgIBul5/sApzsHNny3D38tGsr3x1w4L3tFsAOmaAi3K2GTr712AkHcVHV4mtfRnTobOz7RpAav59DGzbyxJzFiDIZkRMex+/3dzGYaWHk/4zlB5dwTmiHXDIxxy2UysYm9p80sMR4hnYuWsbG+vHT/gw+lutBAztKmpBZA3m6/CSyEkcG9J/P/vj+KBRrSTqxi9E+J2/6flutVg5nHGblqZWctZ6lj0MfOvh3uGofb+8whFz4Pm8RiV8c4Mk7P8Xbtw0VB1ch1klI3dyRy5X06H6MAwmPo1LFc+JkP8wmB7p1W4eLi81WPDCwH1+nnSFt13a4YOh351RQXtCAWNBAb68TdOyQSmiXfPr0WMfEhDIygY9T88mNL0IAlj7aAxe1gloHPyL083mja0euzbX7a/A/b+iL0s+RcewwCrWGSW988F9n5AFMllpEuRykC6t4Fzei+13ZDLlExBEXYVtVmeqrWPD4HezZfgys7xHWZRy6/SlYGiT0dRddR44IghyXnnVo+ndHdG4u1u0gyJBZTTQazGhVcjRKOTgrUZXpKapqwtf1xgNqGYkZgB29bmveEWSmbiC/8m2sRjvaRH5EUc5etA7+dBj4NE2GRtbs/RA742JclAaszh/g+DexNWIop0By58FZy29pnPrcUxzdvpzSWgM1OgtNkgOudjXsPLeTkzknKW8sYnREb/q1vx2LxYRcbgLaYm8PFRW5VFYG8OmnbzNjxgscPTYbjaYQZ6fnr2rkL0KpUDNjxAQeHljL7vhZFJem4OOQh1yhQ66yMW5Mda60DV2KT7CN9ufk6UVtWSk5p04Q2jEOBIHo+9664jmsVisfvPc+i3v1BTmYBQVTqv7w4lYCjUaeTajCKheI1YFagiyZlU+yCojJD6bhZDVGp3M42Y2nImQ9CtXNr1+zyrOYuWUmWVIWAN5488GID67Zr03b3nyR+yQ/ZMxnrWsWVcse49sZ27A22Vb61qxyatMP4hTRi2FDl5J+fj0ZGT+iVqeTdGIQgwZmIAgCvk6OWFzckVWUXhr7sZ8PcZF51tv3CAfi7yXN7MEbB46g6W0rKHJvoAcfiMVglXhgYSKnnx+EBBhRYLL8c/HR/3lD31Bl0wi5f/Y3OHn+vYWMbwafxR+lRq/B2VyD2j+K7hPuuHanP0Dh4MqYZ19i07dfYD4VQcX5OiAYAJlQjkXywG2ICWW3fsgcLzeeUVo1FoOB34/n8lhvm1uj34Qwjn6XyqJ3j+I3NpC7B99YFZyonpHkpOaTtO0sUb1iyDm3jYy8F5AsSrr1WI6rZxTBkaMvtV+66wWCNTvIN7Snc8w7dAiJvcrotwajaMZfqCAx4Xe69LlxiqWxoZqt82aRVKnGKFooV1dT4VRBubqcWlUtHNl1qe3eo/G8mZeHq5srgiABZ0hnAkJVb7IybfLJ3333MV3iNmCxtKdz58ev6xo2bZ3L4fx1jA5uh4FlbJOrKK4WCZArecBbj1jflRETlrboczEWFdTu6vfWaDSw6KcfyS2vBFFk3LEESsJjuSe8IzI7JS72Kryd1biqFRw4W8qeohr8tSqm9QpCo7poTjqjry6j/FAC1gNOBNQOoSG9I1H/ubn6x7O3zWZJ0RKsWBnvPp4HezxIqMf1/yYHj3yMwTzGK1+PY6NrNsf3LKHrhGcxVZejm7Wagg/+g9P8/QiCQGTEeNqEj2XPXltQdtOmwYSEPE1U1FiorUGSy9l24CBOz0xnXaOFkeNn80D9IcItkAQcMgcjyQRqc2shyplDVQ2EhDiRnVlDu2BnAPycNchFgdTCW3cfXi+uaegFQVAD+wHVhfYrJUl6SxCEt4FHgIuv+VclSdr8p74BwALAG7ACP0uS9NVfd/nXRn1VBQDaq2xP/19i17rDDNbVkOcSyKx33rt2h1bgFTecB+YO5fTLawBwbl+M9raxCNexKh7b0Z/fdtXwkaaO6ZKEKAj0autJ+bQICn5Np3pFDpntPAjzvv5U9rAubeCXDKpL3di78hOM9r8gWRR07vQ7rp7N2vTp+cdJSP6CYLvDFBr6M23s3Buf/A1gxYKZjDSkkS14Ye967ZVzazizaylJlTZ6Y4JXAlXqKmSSDF/Bl94OvYnxjKF9YHtejn+QegscO1ZLhw7LcXK29Vc2HKJX/x8QpPk4netErsdORNFK+8jnWqXbWc0msre8hSST8O/7MjNXDOCAzAJKcDeeZVWlhmrJ1q/WLPB8gR3OhkwGWa0tpIvdg4Ipz83mTPwe2g8adsX5zf36S0oadHChr8ZoZv7ksa22HdLelyHtW1+lq108CRg1kbz2GTR9U4S3SkJrf3Mr+pWFK5FLcn4Y9ANdQrpcu8MV0D9qJBuLv6dIZ1uVuw+/h7xPVqEMCW7RThRFBvQ/y/74V1CpN1BS+jylZQlI/t0Rzh/nzbPP8lujBYAV8R+ifuJjDu3pgb1rIeNUqaxs1w+rp+3Zq6k3kJ1ZA8CiCR0BUMpFeoS6seNsKSkFNXTwd77pOV0vricaaQAGSZIUC3QERgiCcDHP/QtJkjpe+LO5lb5m4HlJkqKBHsCTgiD8NdGY60RDZSV2Ts6Xacf8XcjZdJSz728i9Y21nHp3DSnvryFzQwIWk5na7GLKTp7H1GS41P7X5+8GYLg1EZX25ivB62vqsRMcKacQ+3smX5eRB7C3U9BHo8YgEzDozZc+H9/VH4cJgRjl8PuPyaQk7WXnhqls3ziMresHsWXdALauG8TZkysvGzM3NQtBsGVWZia6Y9E5EtdlBZ7+zXU065pqOHPmEfzURymz3saE/n+fTvclZKYiYiXb7yUi295c9auMzAwAxsZ6Uq2qRmPWkHBXApvv38ys22Zxd6+7WHD6TYrMJka7i3TrvgqzpXk9ZVH1pOTUJ3j7rcMjZgPDa23JPuayshbnMetqOLgwjj37o8jRLuOIbAVdV/e1GfkLmFOholoS6GqO4LuOzayWGpWeX3d+2WK8ynybCJdMeWVKriRJlNRdYGBZrThaNcglkZLswhu/URdQcCKbClUJzgp3Fn353Q33t1gsGAUjXRy63JKRBziQadtt9etqC+DWndyHIAk49rk84UomUzJwwGd067oLvc4HSVrNneF1JHokopcbWPe07eV3OLCa78pfYqCbM/3axOFvkWPnYQJALNWRdKDg0phLVp3BarG51t6bEIPZIvH1roxbmtP14poreslGtL9YfUBx4c91OZckSSoGii/8u14QhLPY6BRnbupqbwL1VRXY/0Or+Zr0QuTxBhxwRCc1IDcokAtKhAQrufF7UIo2Q55jzcDcSUHk5IFoxRoAzthbGXULCRSZX+/BWXTDe8yNSwd0cHWAWgObThczKa55e33fiHCWI+B6ZD3lNd8iaCG3IoZthV3JrAkhxCmXUdZvMDTpiOp4O+oLWcWbvtqM1VqM1s8Xe6/DDB61H6WquThFbW0pi1Z/grPKnSav/2CnjMTR7vIanH81zCotdkYjXu1unk1Ua4AKrRMHVTokQaKzfWe0f5jbW7vvYUdZNqPcfOmmysFq8KZHnzW8v/0H7gyYi0fjOurUEhpDG3xCxlNTfRaAtNqXEYsi8PW1BRdz936KzrcaebUSl+ooZsuzQLDSWefES7EvYnb3IrXgJFrRjv6dRuHi6M6HjVV0adObaSvv5qe8+USfiqFPe9vqvaa0GEEQiO7d//JJXYAgCIT5eJNZWg6iSFynDsQnHWfH6i1Mff7qrK7WcDZ1M9Zt6QQ7dKFYVcQDzz93Q/31Jj2vbngVs2imrfutrw8jtaEgpXE0eTPDfZ7AYrGxjkSu/PJzcgqgRjuQj3PXAl+AHdzpdSf/GfE6DXe/yM/rBgGN+BemMC2jA3fRlVoWs1a6nbqSloypN5NyOZ1Xw7tP9cAqSRgtViK87G95XteD6/LRCzaOVyIQDnwnSdIRQRBGAk8JgnAfcBzbyv2KufyCIAQDnYAjVzg+HZgOEBh4c7681tBQVYmD+z+jKSGqm2+n74s90Lq7YDGYSF+wGynfiDFQApUIpy3YnVRzKnU1CVXbATjqePPhErPRxBljDQpJYmKfvjfcf1QHH7x3lvNdYyWTaL73TSYdRs9F+PVZgCTBohPPsK88CKXcRNuKAu7etQ8PUxm7p39Ipf5tmhrdKMueSZlfBaAgOOYgYyYtanGuzVu+4NixKiSrA2X0xi09gZ68y9b0KYx46I2bvgfXAw9dL5DtoWTDq7TrtefGOksSZ9bNYl1AR4Tz59hXvh61Vc6zfZ8FwGgx8cO5bawrPIO/SskYeW+Msgw6dPoOLx9XZnS/h7T9K3EJr0NT3Yaet21BEAQyj7176RSnUifj5riP8hMryTeuQFGvpM/tqYgyGU+tnc0rtQt4KGI67Xra6o3GtmkpIDe2l213+EafN3ki6QUeT3qetypnMmnAg5gNBiRJQrJYEP5Uj9VqsRC/bTNF+fkUFReDaDu+91g8XQPiOFKcQkFGHv7hN/ZcJmyIZ4TDKAC2iyfp6njnNXq0xLyEeeyq3U7nbJGh7W/d0E++/Q0WztvJe40/4J3gh+rwYQCcu11dJXVeU/O69qGAR3h6wFMA2Dt7MFj1AqWn8lFYVNzR9nna6IK4+/gdPHNnR+ojasjNl6FVqogIcub9JcksK6xm7dvbCPV2QKOQ8dB/k3qlJEkWoKMgCM7AGkEQYoAfgPewre7fAz4DWs2yEQTBHlgFPCtJUl1rbSRJ+hn4GWyZsTc2jSujoaoSnzaRf9VwV4VjoBclJGKHAznLDhH92AhkKgXRjwy/1EaSJBI+n8vRo2txVfmiM9iUDycMuLnQxeOL3uUc/syoLcMh/2PWLf8Jl11B9Pnya0TN9Ql3NRkslKgEmkQJSZI4smsfTUWZWP0/xB57dgrD0Hwv8lLqD7wE6GUK1BbTpf4RufWIgUoaav3Ir0zjouhJaoYfHQsS8fXtiCjKOHJkAUePVKNWG5l420gytm9iSMViVJgwFa0D/l5D3/+NmeS//xMymaHF503F+dQe3Ii1pgCHXrdjFxyJqFAi/iFHoujzqbSt38BU9SA+d7RplOhFM28eXoqfbxAH8jeib0oHQCnp0BmTkSkhP/MAXn4x+HlUUOynQ5PfHY/C4ZTt24egEtlfYUXafy+dO2xE61zDgaMXEoDsIMz5WUSZbVW4LGctanvo2mPCNefZObwXcUfCOa7I4J3cL3hn/hc49pYz4ZC/jd31J/z46SeU6Wz3RESgjbcHVdU1ePn70mtIX47MS+F0/IkbMvRpZ5OJqre56pYqN/Pa2x9dVz+r1UrmokUUL11I56I8luoALEjLnyLly9fpMOKeVvvV1jWwdvV+6prqeeLRO5C1UrxcpXXghTbTea7kO+7NeJ2vxUi8gbId8/C7vXU677yMg6RqJuHhqoGqpSytSeSZP+y8v7jzPtZ4LuX77J9okOk4YX+O/v5ljAjywBMPwvybx/r0qZ6ELTzJJ6eLOFtSz6zb2+Nm//cUlv8zbmgZKUlSjSAIe4ERkiR9evFzQRB+ATa21kcQBAU2I79IkqTVt3CtNwyzyYSuvu4fc90A+L/Qi6yv9uCc7865tzYS8vxANC62QGZZagbxv84lpygFZ60XjfqaS/3uHnDjwcHqwmy6rDdTH/U1dcZHcYgyYO9+Hv24AtKHjSRi1zbE6yjZNu+t5/mo4DzVmiB2zs7DPz+HgqgQPnr6SwqFAESrlV2ptgcsfdgkaNLhENOOtuc3Ur3rDKOFekqPBhI96lHGje+PRqviwIGF7N6dxZw5G1CrV+DrqyAnR8LJycyjj75K9Z6fGF3x86VrKHHpwvXVS7p5GEwW7AQDA+SnKZ51GzJTLRZJgbf5MHbChbXFsm8BMEp2VCk7IVqbkFl1+ErnKBM68E3tQ5Ra4bE2I1lXuZ6z9as4mwbIXAn2eYC2jT8RoLQi05wEQBRtD7JCoUWmMaH3OYG1tC+mrTYD3l/ry15LEYr9Gg4NqudEvRy3AhAdfGijSmZGLwNffH8fJ13q6FLtjMbu2kJYWo0D8x5ew91zx3JKlgNAnb2Z0Y82u070Oh37N2+ktqbmkpG/+7aJhLeLufRyuQgftRuncs4yuGEE8uswTJIkkfvpAdp6dKBULOXBZ5+9Zp+LOHb33TieTMZeq+VApIBeCbFu7dEeOo3DC++zec1SCIsgcOBkwjrEkph+iqTd2UhpjsitakTU1OprcdW27gr8KnMuaEFmkej8yNsUrbiD2rd+xmv0w8jVLQkHRwoP8na+SIyYzU/d7+P5hfFkksTaz19n4vMfsHHfKl7JedvW+A+WNCqgddegIAjc5eiAhJo+A0OI7frXeS6uheth3XgApgtGXgMMAWYJguBzwQcPMBFIbaWvAPwKnJUk6fO/8LqvCxV5OQBoXf5+/+9F2Lk7E/32WM78tBmXPBfyNh8l8p7BGGubWP7hq1isJmI7DGPgy09gMZspOHAS7443HhgszTjJV+tegCHt6JUwgGm/PU5Z2WBObfkZWcAa9C6FIGtd7EmSJPYu3EJOcT4lTZuYsPE8eV4+FLbVkOvixrbO3Vk6ZAz2DY3c15TD8OAAzj36JFE/fYf/bcPoMsDmHiq87UdEFRT1+Ri3hLdQr7sTEwoq5J74O0cQ6u6Po38AhYWN5ObK8PAwM2bQ7RT9+iDhlc36JIcdR9Dx3g9v7obfAOy1as4a/XFTncVHt4sGvLCnlGLNAFTDXkbpHUTNjt9AX4esPg9tfQommRPIlJTK+yIf/gZ3n9XycUoujeVtSHhoMf3XbSNDY2VJux7083Ph/T1j+UCq52NdLoOiQgkIs9EZHRyjKT35EbqKOoxtF+NsLMNDZ0e6wzlCXEvIKXJhWfUFup03QBkJlPHb4jhwhn71frw28cYC1osf3IBeV82Tix4mVcoiqnd/9m7aQHJyMjV6A9IfxMAEqwUXd7fLjDxArz69WbVrPSd3H6Nd/07Er91FeHQEod1a/93+MPtZhnlOoMLUxPEO9XRxvT65CYvJhH1yCnqNhvYHE+j8Bynp3LxTnHv4PkL2ZbBbmEl2ViP7OAiAJDhRH1SAuUKOR0MAZovlSqfA12RPLjpGNIQgaV2xhKgQqkyI8pZEiC9T1/NVuTt2gpG5nXvz9K4TVLrYfPmbzZlMNBsuGXkPsyvl8ipESaSf2I1u7fu0em59WhWNh4q5q5M/LkP+GZfNRVzPit4HmH/BTy8CyyVJ2igIwu+CIHTE5rrJAR4FEATBF5gjSdIooDcwFTglCMLJC+NdRsP8u3B6ny3Krm9oaPV40oF4tu/YjlwmY9CQwXTudeP+7dYgk8lxiQ6CvDoks22leGbDDgyWJobfOYOY22yuHJlcTuiQywt1XA+y7ppJlH83ymJP8fi8TQB4ekbQsetQTpaswewtIbayfa2vqGXpT79TaLLRThU6Bbu79OC9h59p0W5IbgZfDO+Dh48t92B3bg4ASrWa6g8ew1SQQ92ZOpy6B+I79HF0cRPJWPYizuVHMcvtCarYRwAiheIgOoYNQNUzlKpTO/FYMho1NkZOEZ5Y799Aj5CbY8DcCHYt3sinRaWUWJ+kX0UiX379OVrJQk1aKl6RsZfulf39r111nEc7Snx/Mo/4nCoAbguO5qOqKhZnltLPz4Xn+oTw+84UvjMFMsHdtkfZlXCM3Uc34Z94Hs+BChS+uaScT6Yx1x9rWTWOsdV0+62auu4CCwfJ+DT4cfrE3c1nvz/OCf15QgQPZs/YeE2VRoCGhlJ+3/U8IZ6xLDy3mGTRbKNPAB+9/SYmUQ5WC65KOV26dSckKorc8+eJiu2IyxViWe16d2Tn7p1sTLL9ATiYncTjLo/g1eZyqQr/hljUkpzjdlsYGnQ/1YUVuPhdnuX9Z8gUCswqFRKg+FO9gKDA9gRsTUQAzry4nJJGm6Cb1rGOkS/3xcttOHNWL8ewHVa8lIKmVz2jRvfC263lnF4b9yVvrX6MTe45qJbOYEq2AXFybAs33bHSZGaX+RApL+PH2C74a905au/O2BwFB13gsPdphs3vCBfcYDNCHmfioJZ1lVtD4wWZDPu+fgiyf7ac4PWwblKwBVH//Hmr2buSJBUBoy78+wCXPLb/POQXqGRRvZvFu7POnubk0aNUVFZSVNeAIIHRZGbD1u3ExHVHeRX62UU01NVyPH4/Xfr0xeEiQfoPKEk6h2FLCWbJTNDYPuyYP5eUzTavlRhx67uLvLIcto5vgxRZzm2amS341yo3PygBqxYajpzGvns7zq3PxKOtKyuTlpBzLh+1RUUHtwgG3j4MFz93tm3aiGdDHWX2NrfADEHHxFED8PC0PZySJGFZY+PoB6t0FPy+DwCNvwqP123l5jQu3oQ/9vul6yg8fRD91rcJKtuFvMwWcPYByu2jSXEfSLec72ka+A7hf6ORz9u2m3fmxyMPiCLBqqVB5o23UMmdcZoLOkcizm0v+2lfFYIgEKRUcspk4JdVZ+jZ2w+qqlhraOJdnRE3lZyeZjlbHS28N/sogVISolVHmzMJ1HSMwj5oBwazEmezF/WmLDBBeYIjgl0FwWUyVFaJBRk/Mqj7vbz52JLruqZzaev48vCHFJrrUYpK0kUL1CSjECRul3uyylxOUF0gFgnaBwcwbMLtODg7X+rve5WqZmDjlY/oO4zTqalotXboG/QkV6eTeSr9MkOvS6ukkzEcK1a6mAei3FJFI1VUjXIlrN/V2U5WqxUJCY1OT11eHo5/ImWIokjauQSKOh9kV95+Jh4yYqgbiZfbBAAmDx/JgnO7EfIc0B10YF76Dl55/+4WYwSFdOCtcd8w7uA0NAZbnEkT1hzorTXqmH62EgdBZFFcr0v1csMbq9gZ8Rxjz8/hkHMe7uZ8ApuCGVg3DFmRH409jGjtrm47ZPa247qUChTeWgTxnzON/9OZsRllldRHdOSnb7/Fy8MDyWolp6wC6Q/b0yAPN3y9vTl4+hxbVixlzJS7kbWiCPhH/Pz1l9SZJRIOH+GF115DpW7mrFel51O/JAuFqMLujgBK9RUsT1tMFLat4ZpP3kb+/iwiAm+ugpLVYuH4jDl00rZhsQzavzSoxXGFxhaPMAVa0ZfWsvvV/azSrqMsL5MCh0zs/Ox4xfocEx5pZkAMHz2GoVYrQ3cd4bRcwzeShkVJ5zk93A1BEGg4fBj/k4kA5M94GgHwf306DvdeWY/Gr10vaLedutwUGnMSsTbVYO8ThkeHMZxZZHPTyByuP1N5VsI8ft5TiWhxRjJrUatMHHv5TjSKK/uMp63NIOuiAJcMRjQdZParL+DgfeWMWMliIXfzYQpPl6NSgZ29HHs/d+w8HJBUdujLqvh2XBD3rsvii6M57BsYAkYLgt7Kw7/toSLTSonMilqALX5W7qi0uWPczG6072/byNaUh1OQkX/pnEH2eqLGlRIFOLV9ixfPzWP6ssHEOIYiE2WMjJ5CZMS4K17z74nfkEAT9qJAk2BmpksXQj1iiAodhrdXLJ1nPUmSzoMH7rmdgKib+91FD+pI9KCOAJzde5Lkvek4ObaMF1hNFirnn0VAwPOxWIrLSmC1LT/Aye/qpQ8BEnMSmX9HAA26LAzzRvJ47xeIjuiNe0Abfpn/DPG1SZx0tt3Px09Y6JYuYVDsor6gHAd/Dxy1Djz16niqGqpZ8sIJHCu8SUg5Tu8OLUXYtA62azlTk8lwBeiqkzi9pjOSScNJn9splgbxY2DZJSMP8IC9jFdELZGdH8fru48wyTrTfvKjKHO1xBYZSPrwMNr7o+kUfuXi304jg5FMVur35qPwssOu07WrsP1V+J829FZ7J5SNeowGPVnlNikEhQDjxowmom070lJOEtWpM7omHUdPpXLifBbV33/LtBnPXnHMtFPJ1JkllGYjRrmSlXN/5Z4nbHQrSZIomnMMB9EFuymBeHQK49Pl73E4spTpk35AoRHY9t6HLJ71GtPf+RZf9xsPxtSbjLzYqRexHdL5tdfdyP7kU1U5+qBOFtDHSqz/dSnV5iyqYyspcLC5r5rkTbj2vLxSliiKrO0fx6N7j3AUBVUqDevSs5kQGYq2a9dL7YQKA8Zg5VWN/B/hGNQBB79o6ua8j/nIBuqrTHhlr6FU8CC444DrnvfSwzn0aNKQ5QIlVXY0mORMmLOYbyYNJ8zVu1WWxay74rhjsy2MFNJQwKfvv0bZvmQaPR0pO1tM3olCKnRa7EQ9voFKQvpFcWRhEsViIDbFtQtIbAL+qLVeS3+ZjMX2Zu6cvYsxypOcaOpMbJMSb4uCMtHKansjvTnF/fq9ZOt70Db2PCkGLbW5ciyNVYAXAgLdPEYTbN8Wi3QvMqGWEfahlHv2ZU7xPlJrUjEJAr8dTOHbmhz6dHv6sjmmZ2xhvcnmEjj0wGlMpiYUfyr4oXZxBx143yJtOS3lJMmHDlFc3IBMEglu37Jwi6msCawSpbpc9n+wEkwSgfZtcfQLI9rv6oSIr3Z/xZz8OeAPnnUKGhVmniz8HAovhPYE0KokQioVPBo2FfsxBkhZgMpkIOP2O3F7+XVcO0ehcnfGXtDQ6TEtST/WEz8nh7hPO6D6w07d0zuE2CoHToTV89Y0+PCncyjeFsG9liUVkagcdIwLaZlEFeftBWUmVqekMFAQGfbsq/Toast5eH3JCZa4QWNBIb2zipkZ5kOf8MvdYIJChvPEcIz5ddRsykLdzg1ReW133F+B/2lDX9/QQHRMDBMnTsRkNCAIYosM2Q49bFQ2pUrNC6+8xucfvEdJZdVVx6ytsPm2u3aN49SpU5wvq+DkwQQ69upNY3YFzqIHNc5VBHWy6cZ08I6F7OVsztjMW5M/JOOu0WQt2sRvMx9D0S2UjrH9GNBrAgr5dbiMDPUsnfoWr4eFM/e4C84jW/epnvFSsy1fTk+tGWphUGooz766iK92fcWcgjm4a1v3lzooFSwe1of4vELuyCznSHEpEyJDEeVyIs+cJv7XT4je/C31wzq32v+P0O1cTu3SeQiClab0IvSlF7Jul8bj0b6ewvvfxEsUMRtNHNtzHF93B4K6xJC+YzO1GYfRNZzDyVyJXuWG5NOJl1zN3FX/EcVmJ+TP7eP+1Xs5k+3JsM9OIFM08thgZ14cMIyKxnrWnzlBW283AmIDmXzyPMuL7MlyD2LeSzsQRQdAB4iIVm/cZWU0mjUkF7iSvLgCxEBi/GvpfE8PjAYLTRX11GUXo6s3IRiaUHs4oa+spyQlm2R5KKc0Mu4zpNGpvhdGJKo0tXjqnHjXnE6kxszO2ucxSlrO6odhXLQWqymLfaOH036IhiEaO3rfNoHyD1dQbXoad+V7SCumM/XtMqZKElgt1NblM37tWJalLWvV0O89uwwArdWWcflnIw9w+AJl4oNPvqCNo54pT72NTHnjWdgrVqzELJMjSAJ9zVGkzdqEMsgBlALUSzhXOQNQZ6qkuM4mxVysy+JO+5cpScwhoPeVeVWJ5bYd44qhK4jyjaKhpoLN8XMoLslgpekowUZHfpux99Juuzz3HBUfLkBvp0TUN9H46pM0/mE8e4UW7ynTKc0P54uvlvDS81NbSEJMGfIZyUnTyXYXye6sR1PgjZ47Kfe0uaIydUbaaJt36kHBYVB2jkaXWNpnLyHnm7kUuziTLtWws/tgGhUBBDVZOS2XmJRfSOe0Qh41NzJ2WFwLmrMgCtj38ad6ZTrWeiOi299XavGP+J819BaLhfr6epycbFKwCuXVaWFqjQZPVxcKahs4uGsnvQYPabVd2y5xbN4Xz+HjSdw+fhzLN25m87atVFaUEX7IBRkyXDo3K2QO7TuW0Xm7WdW0kWfrXmTS2Cc4FhDJlh+/RHUgh5QDOZw+sJsHn/kYR61zq+ec//IwrLX55LadSqD5GCp5FmM9+l22RaxqzOM/ux4gockRWd5ohnRIoibNkwPycJKXLKVvoI0+VtVw9ZeZ0Wwzyqd1zQWiRVHEJ1SJZ2w9NR0mXLV//dwPKPzsdyQrIAkoXUQ8J/XA8b6nSXnyYcpPCeSuO09Ap1KS7p6GX2kOTcDZC/3dXYyEDLe9UI16Gcq6bXSUFCCAj6GWJ3et4KM77qa0rJGFScfZn+LMd9t07E3/jfNFCox6Z6AeG0fAlnko6C2Igu3f4doi2vQPw69vV1QXqK/Z20+Qtf88Wgc5PV6+7dJc3AD6RLecX/pxDMvfw2XwXYh+MbydPQWFkw4Hq0CVTEkXjEQY2pFwEoqVVsplBu7xaaLieBaCKHE0oC9Hgbu8yxDs7HCd1oOmXQdoKBiMvWwXjbvfQTvoLZDJQSanRgC1ePlCwGRo4Jsqm4F8PeS2y45fRO/2YcSnZKFX5XO+LoBfV7/P9CnvX/U7bA0ejvYUN+rpofHDt8IOe6Xzhbz3lijQpbf4/86i33Fb0I6kN19g1NIvUbQiSBbrFsuJwhN8fuBz7u54N33D+jJ57H8AeMpqQRAExD+UlqwXbJTQ0qHt6TvzK/IXbcRU34Slrg7BZEQ6sIV2v39hK6G+F74xr2XyjO/xsffBYjHz0rE30QjwUG0tjmNl+Hd6Du/g0WgSEtEJWvoeTeP5YC9eDPHBbLbw2N7zYAc90vWo7O0JSNqDymwkCvDLK6TjL3Np525PY6OJX47lsKUynYgPX+TsGxrcpz6J4/jhqMJ8bUH/C9OQ/lWvvHXU1dUhSRLOfwg6XQuTH3iIbz77lO379nMwfj+333UXpQX5HEw4yKChQ9Fo7Eg+fhQAL0d7wtt3wGnrFmrNMuKTkimRaelv7kH14Rxcu4SicXNEEAQmtruNTSm7+Hnfd7w49nW6dhxM7Dd9SMk6xp43P4LkAr6dNYNX3/39smsym82UdvUiLK0bKS5K/Jw1CMCw8c1RfkmSWH/mO744+TNlDd7o8p9Esmg51JCDY884TqT4IjvTSKSzbTeTkJFFn8jWKWAAg0OD8E8rIBM51j+IY7l0nIwl/j0K81Mucd4lswlLYTo1X75GfWIGVpMFYzWo3EUCF69FcPVFZt+c5t1p2zFWPfAcMfGbKB28CS9BJOu+GRj0RmQ5mUQc3YnZKCe1z3e0HXAnckFGxodxhFtsK8QN7v3Z5tYbslOYkJ+GvnwZCpfBmKp7cTrLA0FRjcpnJVhVIIkgmPGo6oqzy15G9n2G4JFxLVZ2FxEyrBMhw64dmM1MP8LiOYtY+8x7FHr4IE+tRo4ZkwBWEewkgUSVhURVS4rfW5VqOrt054l+HfgwZSXmJi+eiHRjX5SEPDwSx/BIKjZXYH90F/r4DTZDD9ipXQm1Cuw0lbN8y5NMGvYVokzOqXNreOXgG3CBveHrdWVddm0bgaN1aymSYFiBG/lpKtYfX8+4uCv7/S9CkiTSU1Mwm0x07dWb9Tt2UWLfxNCXRqOvrKOpvIZTGzdTlp2NrxhClaGYsqbcFmNUGooQEKlwt/LFM0/R5ORGsE82g4d+TEBP2+/wmYHPcGLpCQ7pDnHo0CEij0SycqpNR6k1tlH2ttX4AhHjp6L1diPq+ZaFfiqSJpP/00IUCeuRmY0Mm3+OvNVTSel5L+vCNiFzLsGsHY466zBJxx1wctXg7mdGJ2jpYGokRaHls+wSfj9VRJ1KQG8n0i7XQERBHUM3rwC5HJPJxNG+A3BoaCDGw7ZgsLdXMnNgBO1+PnDxBlL166dU/fopyFXIXH0QZC4gmch70IC5tATJaEQZEoL/N1+jDAi45ndyM/ifNfQ1NTUAN2ToHV1cuWfqfaxZsYxak5VFCxdhsZiRlGrWbttxqZ3MYmbEHaPZs28csb3akX3Sl/y6Rnyk3VS5hOFa7UHmL3uJ+Y/tQerWsS+xSdEsqFrGqHNjaBfVEaVCxankA4gXSEl+MS2DZFarlbzCMu5bHE9e9RQ6dMlh7oA7cJ/assqVyazjwY1jOZoVgan2GSSzHZLFkS/v9WdCzC9UNFWzIeUAGqmWgn2HIQpclP5cC6MclPxsUrBm7wHEQ4fx3rCWJkcnFL4xdKtYxCGZG23LjVQuXI2hyuY2EGQS2lAnXIbH4DLzQwSny/nToigy5sdZbJ9cTtj5E5Q+9iKjn5lmm3NjHWlxOzA3irQbfM+lHYvvzD3oPo+gSOPNwk7302DUsr40k/21c3GXC4TYbSPf4RQydRGCzIBWD1PpQaO1kSOmdDJCD9Gt3p/Q0TenWHnx+1iyYStvyx2pH/kg7sZynnKs4pcSm9xwwmuD8HPQYDRYWLzlPEqlDK1MJNjPEVGEsQuOkuYQzaDJY/jo7RW0lfYxrjAaSZpyaZ7uo15Gf3gBMprdKgqllt8mrOOFjXfzXtl+fp/fGTelA4mWOjwlK5+G38fQni8itmIMJUni2w3383P1CdSSlSc8e9Fn6DR+/+13EjcmkpmTyTO3PdPqi+8i5n/7FTmVNRdvAoJkJbe0DF1DPVoPZw6uX0xyoi3IXHBpTwZ977qfbhPuIOPYYeKXLKDdkLv5/tAbxBWBrKqayipnlqd+zKjSwURPmIlMlPHj7T/y4fYP2VC9gTRrGmaLudVSiQCGg0eosxPo0m1Qq8fdO0fi/tN7VJU/xqcff87wg7vxrC7Ex2pPSMkjZLg9z/jUJKrTnQFYvWwxc0QvkMl4xFXDe7UCZSKUOcjwaLLybF0TygM1OOsKEC74+19ZvpGH62pwq6vhh4fupy7AgYkPPUZEQAyqCyUzSz74kK4qe5qOpWDIysVUmI+lphSZgx3KwEC03bshqFTULF9O0SuvELxwYavzuVX8zxv6i66b60VwZBQzX3+LY3t3s2XXbiSlGsFswlGtxNvLC437IRycDnA+awm2WiGZuIZ4UJI8mBjFOfTqhVTL7kVR2RwLEASBdwa9x4T9k/g98Tc+jvoSaNbKB7hzwgwMej2b95/Ax9eb91ccIdXgANh8ri91GYK7x+VR+o+2P0juwb4YxGZmgYdbGRNibHrvbhpnxnrspkfsYSz7x+NWUE2DvGUR510nktDK1fSICKBk/y4S9+6hZ5ceWPclE7ljAzKrlfzwSLTVlbgeriAPd5x3LqYIkNtZcR0UiXbwKLQTHkG4QpLWH6HRqBi9ZgHluYXEhDUHCEWtI+5ju1Kx/jjGpP2outgEuCRRwM6qZ6nnUPYZbbEPk6YDFrsH+X3oSPx9o0hM2Upiyg7G9H8IZ2cf7ByaGRMWq+W6OOhXgsVqJWbbEaodfWlXXsJ7IRo6thlA3Gc2mulH93XCz8Hma1WqZEyb0JIyWpSXh8xqJtKUjyiXEUg5HjslLL1LLjOyCqEOk7UlZdHRNZQf7jnA9gPvszx7A8bGctoK8EplHR4jxrVq5OtrC/hg41Q2mSu4zarhtdvXoXS0BeFfevYlPp33KbWptSwteZq7nvymVaZIaWEBOeWVgEDX6EiOnUsHCawyOZuWLmLy9CeoLmqpbKl1dmHsc6/iF2lzd4V37UF4V1uuyNxRe2g0W0hPyOLgzx+gN1ewadlu1m08ToWdI2EVxbj4etO7ey8Smg6y9sRaJsW1XipQVVBOnbsGeSulPC/d98OHqUlM5E6fAIwysCpApxCZ67CXcYn+6ItFfNp6khM1EqfV8xmcsIkdfccyo14E0eZWOdclCmdHNVlLtrBFrsbfr9nd8nRcOxYMGsngowcYkGDb6Z88VsgPb06gMXsFMwHZ/F9wXLUax+FXz9Gx1NVSu3IVdTt24Dh06FXb3gz+Zw19ba2NhnWjhv4iug4YROc+/cjLPA/KGkSZgYyM5cCByxvXqZnJHOzQY63aw67S4UiuBfiXdcfe05Ok7bvYtW4edINiY8mlbpLZYqs2Iyq56+1FnLBeZMNUAc3p2IHORvqEXq7XszN9AVWbS3GxdCDPCUQsRLsV8/3Dtnqf9ToDP7w7jYDCZHLNPgQ65zKlNocl5xPZ8c1pfCtX4CSrZYClAhm2Vbk3MEoORT+tJTjXjvS47sS+/y7DggORLBZKTqWSkZOD0+H1hHTqjP3t0xGu8rBdCXK5DJ+wy1kgqqh2sP44UkPzS7C6thgtoFO3/C6r3AdSUmklIXUf4/oNoEuHEa2e61aMPMBrJ9KovkChfTfcl15tO5BYWkdTjYHwSFfuant1nXW1xrZC11z420M/irOP7iO66PLrbVR1wdG4h30vPkz/2XOa5yCT06fzDDq2fRDxl/54GauQgA0HvsN38rctDPWxxJ94I/kbSkR4wrkDj435DeEPwX4HlYoJTkmcr/QgvSKUgws/ovfUVy8dt1qtzPv6S/Jr6kCUcc8dt9OmXXvOvfka9TLbd62rq+OrqbdhNtriON0m3EF0nwG4BwQhSRJpyScQEIiI7dhiflq5jE7929Cp/2889em3CGfPEGYuwLHWTJPFjJCfj0lrhlDYkbWjVUNflXce3/wmTve7spvj+BtvoF2xEgFbMSyrSkX27W44dfmMKYW9EPtMwiukLd4BPQH4bPV8Op45SoF3IGcjmq85KvEcSw4nk5FnYw0pq85dOhYUHcXr333GuZNJbFi0kCE79hKVk8aXSbMJEWy/Va/TZylIP4F/xNXdgqLK9tsofe/9fw39jaCmpgYHBwfkcjllWUVkJJ0jLec85fVVBDr44OTiTMfenfGJujLlTJTJyCoc0+IzoyGM9o5RpBls2aihQhweA17D0s+KxTeC/M8fRB5Wjq4ymiXf/0ptWQLaEHA26nGq12Lv0Cxpe+/9rzIsbykVYggX7CzjnSvxdHNmR3k9OXXOTIop5ZO7L+d9F9aks+C3X4gtd6LdSB3Tg7w4mHIf651lvPp7IZ3rHKlLO8ngtNN41ElsH9mfQekL8RVKeZGfcKhsRI+SCsGTDep+aCURg7WQwHoRhc4RcvMRe0Uy5uc5yC5kAAoyGT4dY/HpGAsTxt/qV3R1CM0rp/oaW8RvUngHeqlMHDxdxBxPW8B71ckzzPMP55lD5/hdqmfooL8mu/mPeC4qmOWHTtMkkzP/VDq9O3egg7s9okIkM72KrKpGQl21rfatK6/iWPxpLKKc02ZXJEkioyoZYU8gJTHnLmtvHP0urOmLp7XmsmMOn4Veev0fDRjOQ/KzoN/Pd/NimOTUFl+XNhwsPsR6cwV+gsD8bq8T27alYmRTYzkFP/Wka105Co8IqsqdOZaloKfZiHjhZXD62BHya+pQmo307N6dNu1sbsW7H3qEn+b9hmDQU372OALg6OHFgPsepk03m8E8n3KSlatWYbhQ1Nx353buffRx7OwvL1zz7QtPYVNBB4PRwNxvPmJP4w7OBdvK/HX2bp3dJWi11GlFQo7kc+D7T/D1DqcyOZmYJ5+kPj+fjHfexSU9nTofH/S330NGUgJ6v0hGmGOwJEDQqwNQOTZ/X7sXxGNRaRANOl7r0h+NkwO3FzTryJ/L9SHILZ/glCXIsgswJO1D1dm22xQEgehOXYju1IXjixegePcjJqWHMvPzdZzpvwbdM69Te+9U8h+7l2CjI+bkVNTR0bg/+QSCTIZksVCzahXVi2wqr5rO12a03Qz+Zw39yZMnAZjz/rcUmSqxXjAczjItWQ0FGBtySFlyhvunTMU7uvWVwR9XSQZ9F0JCHyA8bADm6lQ8jh+kXF1NlnQcN1kJjkE23W95H0d8TfPQ6ZUUrxyOxVqB/+mJlIjp9LVX8NiwFy+NWW8WqBBtBuv5jjIevX0wSoWC9KJsfvn6DJ19Svjk7gcu296/+f6TuKeXE2twoipUxnP3v4MgCGxNccPDXMFBqRcdTn2H4OTPsZBAfDQmnrnrTZKP9KV20xOYrS7UWFwJHPgCQcMG8GePfcO8d8nftgT/SXdcMvL/FKy1tpW8oG7msTfW2XZBWidvRoV3ZVQMeKzcyEdu/szzb+ZyHy6r5K9fC4GnVkPWkDieWbiaZYHhpK7dzcs9O3HvsHAWbErny6M5fD3i8qzPX39YzUfZMswXZH8bBSX5BxPIimtPgZ2F55Yfgpeb2xeX1XFk7lImuICv+gzo6+GC0FZuzgkucrkSoqdh32My7H0MgCKZyNcN56DhHIIk8bBjWx4d+RNqTcssbMlq5dyCEcTWlXO013S6DZuNbvlXbDtTTf7RDQT1uh0ANx/bztLf04OBY5tf6D5BwfjWllJbVoIAuPj48eCXPwFQVVbKwp9/ospkQZAgJsifyqpKiuqb+HzWx4T5+XL7Aw+j/JO0gdlkwmw2oVSp2OFykAyPenoZ2/HYiJfpFND6Klgl6Pl1ipwJG42EfD0PAzZuVdaKlYhWK46iSENcHIURHTlVXoBjYBQjm9oiCEq8H2vfwsjnZ2exP3MnhLYjwsGfIXE2DZqSNu789twCzDIz02ZPRO4wDENiKFn3PEHWvY8SfuQgCofmJLCKqgLqfpyNoILbp7+OIAi0638byT/IMc54GZdP5lNzoW3Dnj0oAgJwnjiBus1bKHnTFnh3fehB3B5sVQD4lvE/a+gvwihYiPFsQ7d+PZGp5PhEBCBJEqVZRfz2+3x+XTYfb7UbSrmCcffehpN3ywy+bl0PcyBhFDJ5CrU12SgUI1F4dqXDqOMUHX6es01rES+KKEkS+Q17QQUatRGC9nPe2Q1lschvkYfZOXolHh5hl8YOcnFjTPcmNh6x5yQNKBUK9pw6yDe70wEPHurtf5mRX/7FJzin5GJQGfEIjuKZ9z5DEASSzh3guLKerlltGV3yK6phsbz80PvMe2Ak9k62bWFs94HQ/SxXQ2NFIUeOpuIL5D33PtGjWpeF/btQu2k7MqUVZdvmlY3cbAt4Wv/gfrirX3d+OXgaldXCEwoLr9l78ru9+98qdDz7jtFYZ3/Hxi49mXkuj9O92/H7rkzWJeTxWFwQbd1bFpH4JU2HVqZkdk8nNHIr+xbNJX1dAL3P5qJt0iDXF11qazGZWDX3CBM0I6k05OKm2ghbXoSJP5KRnkD4Ypuue87DR+jtb4sB+Dt3pqAmCSd1CHEqJ/o7B9EuoD8RYa2/7s4d/ZbOpRkcix5Kt2G2ilRBHXrBmU3kpJ+5ZOjj16wCIK+sghNbNyBXqwlsF4uThycKAUSzTTpgwH0PYWhqYsPSRZzOyUMSRNy1GkaOHUdYtE1WIH7bFvYePERaSTmz338PPw83ho+fiE9QMKUFBfwwx+aeWhWyCuQQIHpwZ7tOuJsq/3z5AJzJ2sHD+56l3lOk3fhGQjbao2tywv7VV6j78isci4qwPPsMtVpfUs4fxx0H+hvaYi/aYx1gh2OILRvbZDSyc9Uyjp5Js9FYAQf/lkJjMpMGe5oQlTa3narLQEwjw1BsySSja29ypvSm6/RXcPcNI3X5z/iUmSl+5V66RDcH/WN7juP7j1IQf1qM2gS3z/yegscep/iVVyh+5RUA5D4+hG3cgKhtfVf4V+B/0tBLksQ0wwDsunjhOSn6suOCIOAd5sf9d01l34ad5DWW0KQ38OOPP3LvnffgF93Mg3dw8KBr3AKOHnuYxqbP2LHzFL16vodW645L+L0IyWsoOP0BEb79wGLGIDOhMMvQI+HVq5ZzpQa6BKewIupDPDwuv5anIu1xPfMB67MfZP7qNbx1VAl4oJbpibLup/joHrzjPkQQRRpra8k5cQxBLscn2Jd73/sagB+2zOL7soUIagjqEo0uIQ22n2SH/+9UN4lEhl1b2hagqSKfn777Br1LKBM5BUDeoUME9ux5E9/CjUOSJJoKjDh19kWwb86kVLvZeNe1ZZkQbFvleXp6cHhIV0S5DDu1mvgfFrA1qgPp2blEhAS1Ov71wvqneqsXcWLbTh5eOg+hqYnlQ8fwXFoBPTp4c+hoISM/28e5d0egUdhcFhV5RZSonQk3VDJs4gAAshZ9TVZ2BvY9GshsPIVXWBTF039GownElQomaIJIcGqkU6/HcIvfhTF5Bee0EUQd+gCAg2F30Mu/OdC7bMT3jN06k8qao+zSW4iLeYCIsNZZKAB1FbaXvHfc9EufeYZ2wE++iL05jnQuycbBO4Sozl04u2svZrmCHYvmIzPqLx8sqiPL12/EsGY9kkyOnSgwZuxY2nZpKTfQd/hI+gwbwbyvPqewspqcqlp++eUXVIKETpCBKCOGs2y2WtGJIvnWcp45vxDOL2TsEW96+PYgpfg4OYZKBEniJHqUEizs9B9cEtYR2G8b2cH9CR4zBuvIkaSu3sLGs8mYKMLNas9QYwc0koKGdjqiRjS79T7/+CN0Vtsuf2S/fmzdd5DEs/GUfV3OHVPH4uiiJShIz6mMAFa+vII7PrsLQSYnc2x7DhuzmbbLSvDSBE7tGE+7ZatpOmQrYuLb/nKRwhXVu6gYK+Otnm8hN3mgDAnBXFmJJiYGVVQUrvff/7caefgfNfTWRhNySYadz9UNnE9kAFMiHwAgOzGdZRtWMHfpfPrG9qTfhMGXHnZ392iGDtnJrt0PoVBsJ/7AAdq2/ZnAgJ54G7wpVBdTubsjVqxYFCJ2BgVOUR9RlPk8D3kI3DX4R9Tyy7/INcs+xLptK8FdYnAtqeato7Ytc1fvRIYF7iWnwcZHtsvogGObu5j7w/vIDDqsvmHc+95XmM0mPl7zKsuatqI1yFkxYRUBnqGkdjjOttfeJm3LeiQEArsOuOY9ayzLZe28L6mTHJk2Lg73x58kc8xYal/+D15bNqP6m3+IAOb0EwDIvVpq4IQExdIk02A8vQ66NQfn7O2br8mqUiFarZgu1OS8WWSlnccwfhwZPfvg/+h0Cg4eRuvmSlNDIx5z51Di489LzzzKtvQS1pTV8J+OXhw6WYxgtJIQvw3XUwsw+nYlaU8OX7udp494isZXvkAx5QvatO/EfFM1L9WOo6OqFuG0AgfXiwFmLfXGarqMaEN4p2gaHL5Dve5hOhx8hzSHCBwfWE8v15bSFY4qLfvG/8x9+77kRM6vZF0jEU7rYVtly5feg/nlAuRyFXKlioH9+rBwdyoHNi5h+LQXie07gJzcPE5kZGFy9cLaWIto1CMz2Ay+3isAi0VCI5Nw19rRqVs3eg4cfEWNF0EQePDZ5wHbCn/fgQOYBBk+Tg60c6igV9E2epSFsattB5aUHeVBn34UNhYxrzGTDTlrsbdKhKCgFDN6ESbZBRDb4R6s0ZPI+248ITmLKfw6A69HV5FSUIYJC85WO0Ybu2C0NGL/UASBUc0v/0M7tl0y8o9On45PQCDuPuHs2LyH/KpzfPVFDsMHjabv8/eS88waagyul4qnmq0mNncT6TRhBD55Lrh/tojKIeMJAYrCnOnf/vIY0T3R9/BV0lcsT1vOpLHLCdvyj4j3tsD/pKG31Niy5mTO11+9JaRLBI95Psq6BSvZm5JAfUUNYx6ZdOnHK5OpiIp6gpSUerTaM2RlrSIwoCdRg7ci33cH+YoMlCYIlmJw9h+MW8gEShuSCSxfwIqD9zKx52LsFc3pzgaLhTOWNOgZidVqh1LtiIumEWeHKhzbWIkTnHE2O5AlT2X3gW38tiGZXhm5yESRh197g20n1vJ24ns0KIzILAIfx71PgKdt5XviiE1K1lCsw8Pegu/gVoVGm++XycjiX76iyOTA6BhXgnvYCh/bPTcT5Ucfc2LmTHr8/HOrffVZWTTuj0cyGHCecifym2Q5AVTOegWQcH5gRovPVRonjoaMpWvmGoxGHUrl5WnjxVoHrKLIihOnKa6oYmC3Tsiuwg9vDdUVlWQ99TR+QPihA3DoAH9Ucyn0CyD66y/x9/JguUbLmKTzVCZ8y3OOpdxZvwOv/TUXBtpCj4tMWBlAAzkbbufRAF/sGh7ms7xZxBrMtJFPYL9SibteiVqhYdh741BobXRa+8638+uONxl0VM5ej7cYX2uFVnTBXt6bxrG0HcjV4Cw1++TzsnZhv3gKFSoNoc+mIVdoiOk+g+Kd7+JjMnL8t0HEPZwAQHDcUMIPxnOkwIUT77/NvaN6oXL3hIwsrPZOSC4emCUrCCIysxEPZ2emPfkUas3lcgvXQt/hI+k73Fa6L3vnXAIOfEylKgjvp3Zyv9aF+yUJLjxzo7K2U1WRRreODyFT2lFRk83m+PeY1P89AESFCv8Zm8hY+Cxh2Qsom9URs7UPcrENd0yZTMOvqZhFI/bBLfM5duyPB5mc0QMH4BNgI2OERQcSFn0/B3YeY+eBTRw+cphuA9qjUeppNNpj1usx08j3+dtwQmJY16dxHRrE6bB25OzegGCvZdgLX122EyxvKmdw4GB+OPkDLup/ri7GnyFcjHr/NyEuLk46fvz4TffXpVZQufAsnk91ROl/ebT/arAaLaz7cRnJVen0a9OdQffYfpTp5zeTn28zQAaDM316b8DR0UarkySJxsJdqN1ikWta6s8s2t2H5wVb1aK3/PQ8HmHb2lktZtY99DTK6EAW2rmyJ6ZlMk+cLpsvNUvIlRJJW/o9JuNJzLp42sQGM+7Vb7n/p3EkqbOZZj+emRPeuVQw4uCJbRz6+JtL4/Tp5E73//x2xflKVis7571PQr6VO7r50W7UIy2OH3n4ERwPHMDSpg1aOw2STo9Vr0fS67HU1yPpdJfaCioVIatXoQoL+/Npromm7cvIffptHNs54bfq8GXHj+z+nu77XyF/+lECfC+nmmaUVvDy9ngO+gYhiSL9CrKZN3k0WtXl0gHH12+i+ptvMQvQ4OGFd04WEuBRWY5RLqfg7vuwFhehUatpdHbBe8hgVJJEeOcO2P9BHGvpnt+Zsu8pquVOuJhtdN6U/nNwcPMlaPUYRKxUSSG4CtnMcXLkK1dnnk8di8KuG7NC3sRespIw7dQVV8INZ06zbFYq4fblnPXQMXb6OPz+NPd2n+7CaNmD2nsdmL049dBOJIuJqg88cbugf5PsFU69Qo1nVS7e+gYcrRLnJ3xNm47N2aQWk5GUTT+zLzmXWun/a++846so1sf9zOknOem9QSAFQu9IBxGQKnpBUbFwLddy/SnYe7tee7uWa28oiBQRkQ7SNPQSiiE9Ib3303d/f+whhSQQisjle57PZ7NtdvY9k9l3Z9955x0jMiq02Jgx62a6dO2K3WajqqKcwJO+ts6W7K0LCN90HzXaYLzu24rW+9zmdU7/+V1i9j7DPimW+Hs3YgrxJWf5bop2FvAb+zALC3a1ht7xcRw8loJWwOMvvtQin+8/W0Fy7j6uv+YWuvTqRMaPS1m91o9p12lZXfcF75Xt4bNeDzC4722nlKeoroi3973NLxmKd56X1ouFUxbS0fvczIqnQgixV5blAa2duyRb9I6qM2/Rn0ClU3PVvbOof+tLtqbuJGRrKDGDYkhNeQ+DERyOyYwY/jReXo0VUwiBKbL12DhVYc+Cy3X++TwDX+f9yPZR06iwW1k7phursmOZGp7E85pMegaFk15Zwu6qOn4wdiFHPZAeB3+lUvqZLHMOcSEw9ZG3sTmspIsCoi2BPHhLY8wSm8PKxi8/QqOVGDznJvZ/8h2efm2Hh5UcdlZ/8hy7i7X086+n+8TbW6Qp7BCFMJnwSk2lwVKrUiHUaoTBgGFAf0wjR2JNSaF65S+UfvwJEa+9esblXvgvJXRxwCMvtnpe4/KhLy881qqijw0JZMns6eTn5rEgcT9vh3dk9MotzDUKrr1iJBqdjkNbf6Po+eeJyDuOJ5DSvRfxB/dS52miqHsvLHleaB64n8kTx7dL5hF73yDd1Ikgjxgo3oBT60tMt5F4BPpTv9SfWm0MIU+tQ5Yk/nZ4H5P9PFmyIxdVjZqu0VoqZesp8zd1686cL7qy6+mfWTK0gn+vLiDntsbfXmtzEBVsIjm5Hzr/bRjUSqf73mU3MUCS2NhpAL1yk+hdlNYs3197TWVMEyUPoNbq6Dv9n0T3OcSyrz6i2h5MtKMO+5b/Qtd30ep0503J5//2PeGb7qNWE4Dn3evOWckD5MZdRsxe+LnzOJ4N8QUgYkpfvjjwiyuFUjb70zNBo0WWWp+FKicvG73woksvpWPWVq+ME9AHhZCam4aPJLeq5C0OC09uf5KjZUfx0fuQUZWB2dHYCHpv7Ht/qpI/HZekondWWkGjQuV55gN5AFRqFTPuvpGP3nyfpRtX0KOgAJ+gFNTqGYy9vP1KzOq0s6DYjh4Luy/rxp37trDD1onHkzaxscJKfofhECaRXjeID0aMBmAo0C/zIIsznXBwI4GldqqtRzFptYx7/AOERss7y56gSm/hjqDmHjEff/YUHkV2vCYNJN47mP2AKbBlSGKAqvwMln71Hjk2H4aEORl/+8sNn8wnyElMZL8kUTt+HDPmzUMf2PYsQZbkZKpX/oLsCoh2Ms6SXCrfew7LkcMInR7T6NGog8IoeOU/yHYJh1mg9QLD4FY8RmQZ7x3vUGAIJb5b252NQggioiJ5KDKCjktW8LzOh3lGX44tW82s3l2pnzcXvU5H1vBReE6ZwlXTp2B1OtEKccowAK1RmnuIiNosErv9negjX4EAtb0Sz/8qUYA8BZR1VkI5C5UKP1dMdJ9Be6jZGUGWQ8+9wd3bbM2fQKVWc9m/p5Py8jpUVXVEP/YLXsEeOK1O6qutIIMucBsqXTl/7/IwSTveJeaPtQAMu3Yxeq2J8vIUjF5hVJalUZ+/lzED7252D4fZQunB/SRvPIzNrkFVcgU6pw/5gCP5GD3OqGROTVHiIoLW30u1OgDjPzag9z8/sV36RcUgA7Od3wCvAMoMblN6Xs7KQ5uI8YxkyKTBJO3ZTU7OcQYNHdoij9KicuqlMuIiejcck4SiIh3VFfirDVRJgoWbHuX6JnrA7rTz4JYH2Zq7lU4+naiz19ErsBc3JNxAF/8uVFgq6BF4PkvxzLk0FX2VFY2v/rQP0anQmwz8/ba/882XX4FuNQDDhj7f7uvXHt/DA2l1VBDOs0EpBBsvY9mQySRsWs388iAQOnSyBZvWwDH/5ra7hKhu3P3lnWzN1JKoHojBpmfilCEYw2KxWS38UL4StLB913cUfp/ItU+/wKqNn2P79Q/qogzcN2Me659RWmzenVsPdrVmwYfk2UxM7x9B7ym3t1pW9XVK4Nc+/fqdUskDCNfIPkdhy3CGUm0NmRPHYa8FrbfAUSdRtf+HhvNqI+j81IQ90XqMe9lhpVNNOtviZzPGYGo1TTNZhODamVcxxWJh/E+/slJlYNycOWiB4M8/Z0RCY6tY346QDa2RtfZfGNRGfGs9UAuJggHvIZAx7XkJm/DFofLAZ8jUFtd1MguSgI96zaHfoJZfUG3xw5XBzFqkhBuoKa5H46dHZdRw+xWxbMv8hVwHbD36EffmbAMg1SuQOKPyNecfrCgZY2QARA5uyNNRX8fejxZyOC0Ii+QFKCY3P10hk27rxC/LcykqjqOkoIKgsHO3L5fuWY7/2nuoVAdgvHMdHkHnb3JsL89ASj2NROZaWfXQjUx6QxmAZBGKK2h6XS43db+d2O5tT7ySm1UEAqI6RFKWV0v24TIStyreX8eXfskD815hwYZb+f34Zq4HjpQeIco7ivVZ69mauxWABZMWYNI1r6MRppbTLV5oLk1FX2lF7XP6+O6nwyvSn1vuvo3tiYpSqsk+hH/swNNcpbCmMJMK4ng2OIe7u18LKAG9FvTrzaNH9qMVMoWiEwU2J9HG5iamQ7/u4ajJwpGxWSCga64H46mkImkTzp/u50NtEd97e5GQ1I1qbR3LnlI8GsydTTzy2HuseuIm0gslhg7tin/fCS1kKzmwhqxaDXE+DvpMvaPF+QZcU61l70on4RqJwt+PUJVTRsINo1sk1XeKRh0QgHnffmx5+egiGsMCOI9uxF4Lfr11hC46iFxfQ93PXyNb69HG9cQwZOIpy1JoDWT7dsWv+GCbafJqLZhtTmKbjFD1MBjQ25zkWI2UaE30/vRtOie0NPucKTZrHfEF2yn06UpM1ueU63sTOmm2Mj3hlFtOea293IpRXUW/waco91YY3Kc3nxRk8NnuUvaZQ3ijSz3Tpv2NtOI6tu+djrk6isnmvThlgblEh/89/z1tnulrtrAnpTPhpmy6DbQRPmQQOGxk5AaxaWsR1loNAhWHdqZz+fRWTb/tQ3Jy/Os7icpeQoXwQ3/HWkwh0WefXxtssv6Da+V3GOf5C0e/e5guM14mJyv79BcCNpuN4/mZAOxZncWh5cro3IBwT0yikEjnLyxdtBIC/OjpG09mVSazfmk+T+z2WdtbKPmLhUtO0csOiT3OvQQ5/Ami7dCt7cUU6MOwwfPZt/dGktLvYLDfaowBrZtDTpBXV8Zhs6K8R4X3aXauv38kG0YoY1E/yC7ixYwCogzKS+mNJY+QWJlCrsjHEmylW0EYKiFxOLKIG6Q1vLDuO6521hHohMDQm6n855X8+vE7DXnf8eAbbHjxLtILJcaM7U2/Oxs7myRJYuu3r3Iop4IyhxENGgaPGNVwvqy6ju82H+K6Ed0I8VPcUvf8uA0idaTV9SDx30s4mqHFpvPBELSfTuNajloMfvghCh57nOLXXiPy3Ua5VCHRCJWMrdSMbDMjPLwwXffP0xd+EyrDL6PH0W+oMNfiZ2x8mCRJYu6GZH7amgUOGb2/gSFdAnlvYndu/ymJtBQ1KnsdD476f3ziE0qw3YlJe/axb6w2MxmfTCLBUYt3+X6swhPDLV+55qA9PWXlOnyN1Wd8XyEE4yddw+ARtcx5axEP7Ajl4yPLSK41IsswM6WUHumppFhddXPr/Vj7h+I5fAyaDp3wnHwT9tQDaOP6NOTpqK8HDIz750hM0UprPuVQFtsWZSCcWmQ0CECjPTOz1smUHlhNVPYSCrUdUI17Ab/QTqe/6Cy45vFnefw7Tx46/h7dUj+h+N+rSeVqEGom9m1p8quoqCA1NZWUlBSysrJwOBwIVER0DqBn/6506B6AyU9PVW0h1ywyUazRYJQkekaNpNRc2iyvpy97Gh/92Xuc/dlccl43ljobv+1UBib5rX+H3i9MRK09s/dZcnE66QU/M7n3Aw3HKnMOsS9lBr62EfSb9Fmb1y7N2s0zWXVUSF48HFLM3O5tt1ZlWSZ22yEcksx7KQd43fkhlcY6Amz+3GC/hjvvVe7f8+vGz80dqYVUeCYQ+bjyqfjZ/TdSXlqJ2iEI9a6nsNqDIT18GPr0dw3X7FlxJ/ZDR9nl1QWvGhOdosIYNPXvePgGUW+18/SCraxNq6NO0uAp7AwJ13Dr5T3Y++WPrNN7M7Y8Ar3cJCiWrZjr3pqM3relR1Nyn74gy3Q9eKDZ8fJX51L05RpCZo/G/6nTtzZP5sjuxXT/5XZ2X/0DA3s3fqVcv2Q/iXvy8QrxoFOoF8mZFdiqbQgPDXK9A78IEz0ifNi6K08JcOWnJ+XR1jvOW6PMYuOO31LoajLy78Ex7Pr9Owatu4cylZEAycwfEfNIuOPZduVlzTrI568U07/rcQY/cPZD3Z1WMzNfW8y+OsWs8MplfvR+7DbqdEYKJ87A22Clw/ZlmPMa+0t8+odRtbcAnb8KvysvQ3bIlJg6szFjKNOuVWGPiuan9/agtfmCSqLHhABGX9X/rGVsSkHiD4StvYOsuDlE3/jOecnzVJirKjC+HQ3At+JFrnvkLrRGHU6nk5ycHFJTU0lNTaWkpAQAf39/4uLiiI+Pp2PHjmhOCvvxyNZHWJ25mus6TCAhbBBltkoOlRxic+5mfpjyAwkBLQdC/hX8n/K6KVHDispZxBfkMkX2Ztu06xm9enG7r1+w9Bvqjn3Ai/Fvctvypxkzehr1+yq4cu54gv+YTpFuGfWlx/EIbNmJtDRjC/dm+xCAxILYakZ3OLVJQpIkJJsTgwb+yxvUGnVML+zJi48tbDX9Hb698HgxCc8m9vTb3/2OF//zLzx+20GdWWmpJlx1G1JdOQ6rhbyqZPrtW8TEPq/Sd+MKPGwDGP34I5RXFdL98aYDN5SqEGpSsSlPZsP8I6CJBycMClfjk1WJWsj0GeLDb7uD+e2ddVz+3N9ayKjr0AFrSkqL4z4Pvkbhl2tI2bqVwU5nu8IZA+QWHCN9x3zijv2AAzUe2uYmuYNZyiChB8fGcWuvSKwOicu/SCQvs5IJQ6L4+Crlq+7HHmHM/WIP1kor2dVmOnqfegq3FVmlvJmcR6bswGZQ83t9DRkbj/BocSIAAZKZCpWKyp4zcTgk1m/L4ei+Inr0DWZw3zC2bD9OabWVTtE+XD5MqStFK79GZgrh8edm71brjfQNcLCvDp7oVUv8YmWCDk+bmbHPzMXoacSZNZOUiTMAgVDJVO1V+k5s5RIFC3eQ1PNuyv27ARJGHxMlxWZ0Nn+caguznhxCcPj58/mWt76JBT3+o+8+feLzgNamhE8oFwaumjeHI8eOkpKSQnp6OlarFZVKRXR0NP369SM+Pp6AgFPPZ7s6U+mjW5SzFnLWNhy/pdstF42SPx2XnKLPs9hY5DeTp1JL2Ji9B5OcycgvF7Hl1mtP2zkryxLdn32XtLB6uoYmIcsq6hZuItXmzeHZ/bjtpc8pSl9C9qEviO5zJ0V/rCa0+1QMPop72OLCMsCHpb2j6erfcrq0pkiSxEf3buEhYMWQw+TqYUyVX6tKvpPdTqZWy9U957T6Gyw5qXgAAUHx1ORn8fW/38ApK5/bMb6l+IWp0ZVJdFINovN181m/YTE2BPB6Qx6bH7iMYF8vPAw68koqeHrBNtIrJbLNWkREAHOeGAtCMSGkJM4nOT+EftnF+HZsHiNfdvluO2pq0HgpLf4DiT+R+9rLxACrOsHmxVN4dObPCLWGkq2rUP/6LNYuswibpfQ1HC/K4Nj+5fhmrKd3yU4iZSd/BPSjcupHdO82ptn95k7syr+XHebZH5LYkFHKc6Pi+e3OYTicEpomE4ZfHR+C7ea+PPrNfv6+7CAbb205VB1AkmXu2ZnG8vpaUMt4OgRzPL1ZXlLJZg87b2WuaUi7u2Y8qV8VsuerAlQIDEBaaiZpP2Q2pPljWzF5+TXcNLMbul5T4DDY7WdmCnHmZmPdfwip3ozn1MlYJCef54TQTVfI7KuuwzxgIMXTlPkHNk+/gVHLvsUjugdx61aijoxBriii7OV5GAcNp2blUuxqE5VqZSjYkJ4lBPa/Ah+bg8T5BaidBopzy9FqNfgFndkYlNaoL0wlzJxMavg1xEece//I6ajIS0f16Wj06PhGvoHKN98EwMvLi+7duxMXF0fnzp3R69vver38quV8cfgL1ELNoLBBDAsfRr2j/qLoZG0vl5yiz7Uofq9X3jcC8xE73/84HCIdfPLJE/zjHy+3eo3N4SDh881cGbuekdPvQj56jDW33MX+t7eQ6LmW4OJs9g0z83ONidj6ePI9viF/33wQMtmJHxCon0B0rzupdarwopau/n3alK/WZqeg2sK8z4dwc9HNFIcMINfxLUj+PDb9wxbpFx/4lM7HBtA7rJzIjqNbzTO81IEFyLalM3LmJLYuXtdwLr0ykOApk/h56j1s//l1rIBaY8MIfDR2Hv/89VUckpa1h7Zx62jFJBIR5McX909j0Zo0Ht18jG5RPspcly4GXt2FVStq+e7lw1x3b2cCe0Y3nPMaM4aytDQqv1+EbcxADrz6BB23ZRCuFxTMGY8pNpf5tSn4r5zDVO8b8fv1TgyqGkh+gbRleirLfmVA3gaigHyPSPYm3ELnMQ+QENS6D/Id3SOINRmY89lOtu/IY3ZBDTvuHtFM+daC6QAAIkpJREFUyZ8gxFN5uP3acLt1OiWGrE8iRw+B1U6Wj0xo6Nx9Tpb5Nq2Ie+M+5PKM5dQcH4ePw4dqLYgoTzpGejFqTEc2bsqmuKiObv1C6BjiycoPkqj+rRB5RgJBl41At3Atx5Or6HyKKM+y2ULVt6uQLTbAQV1eR8AH8KFy/14+pxYAo1qPh6cJj3gTAcl/sOK+p4hbv5RtXyxhwv23oIlSlLnwDyXo9QUAmGbeQ9K6hYR9uJO80BGUu6LxanUa/LtA+THY8kU2MllofGxMuKU3nU4Tb78t6nd+jcdqZUJz/6EtQ22fN8yVkL4J0jZgOLAEPTa+4W8YQrswJiGB+Ph4QkNDz9oLL8Y3hpeGNx9c5cdfN8r1bLjkFH2eRfEUCdfr8Bg4iZieo/nk4wcIL259jtTKqhqyPuxMutVGT9uXDBT5dLh2BCq1iv4PjaE/Y3htyQ18a/2cLrlHWTdqKccPfEOtOQW9RxDlddso1CyhaP9SvNVzUbVSAe5ZcwQPlYp/Xx7P2C928HLhPLI6CRb3+4YkQw4qSU9f3a2Ed4hvdt2G7A28/dsn3F02FK+qvogm06o5y4twZh7Cmb6baSNiWHAoEU2BvkHJWw0yDoOKLpMmMHSq0vE5fOrDfLtxMQaphkFdvsZmq2J+lIkHl6Tw8sZAUgo/582blLQOh5PPt2ZgQDBhSBTJKzM4fqSMwDBPasq1DT73i95Po3ffRIbfdT0ApolXUvbpp5S8+SbSW9BRht2jQqgYPYgap5WOniMJrEnlh9I93LZvFfWqEKwzliF9fwuxSU9Spzbye6+7iR00m/CI7oS34+Ec0zGAtQ+MZMLrW6irs7eZblGyMnJtYGTrD+n/+z2VHD30t6pYNrkv+iadtkIIbooL5aa4UGA81TU2PAxqNCd17N50Y/NwxZu6+yIOVvLa09sx+utRyx5UVp26M9a8cSO16cpXokBxcfX0P4I2KgBntZXZFviywI8SWyB2qwOt3jVfQIVixgqJbXRbNFeVsfWumXimF2K0Ash4WCFv9AcA9JrRaIe/fu7l5GYVknLgOEVZVZSmqFn1URK3/MsXk/eZhzuw5yU1bAcumU7GplF0vGfZuYe+lmUoOgKp6yB1PRzfCbITDL44okfzmzWWmbOfxPMCxGf6X+HSU/RWG/5aNR6uFp1Rb8RvnwqhyyIpKQ/ttVfw47RwDnmPx2/wIIYkLsCWOoUSfQ17Hp/c6iddD7WTz8Juw/zDVFa+t57pP/8HoVLhsDnZ++xIAj2t7Ajez6a4y0hQNbpzLdx7nMcXN1b2VQfzqa+0slCewsrs97my/kMsdR68MPZabu43stk9HU4Hb+19ixqThbXRJRTrq7liyRuoDi1FY0lHZ7RyQsVEAY/6wvUdHqfv7kTUEQaeeGNRq+XjHzgOfdn37Mz/gJmXfU0ssP2Jwcz5dD4/J4fzt5Qkhsb34qNFR0iR7NzdJwovk46NK7MASMmsachLg4QdmSO7ahl6uxNZwL/+O5cTkXV+ukzway8Vhf5lYFPsnBRtQnHl0JAuYol+4BfWJZupqXyUsLBd9Lj7SYb6n/knsVMGNIKaUjOPbkzm1bFdW6T5R78OrEk8zue/pvPgoOhmrf5521NZaqvHZJFYMbHXaePkeHu1z333jtt68d4ru5AKzcilNkBQU9f6qEwA2WqmYrsacBJ8nTe6PsNx5GSgDhmIMChjFXyA+7/ax7vJBRxJLqVP71CO7z9Cx71b+aPHMK6ZPAZJksj6fS0Fjz1Oh1Ir2f0jqPTxQGOTsHWKhOOg0lUQ3L+5N0pkdCiR0coI2B0bD7FncTHfvrSZ21+agEZzZt5KPtNfx37FY+StfReyttO5fAsZ6z+m88R7zygfAKw1kLHZpdw3QI0rxHNoLxg+F+LGQcQAvNQa2t/V/n+HS07R51psRBoaH0K7zU6pJZcym5nsD48xSqVj+OZ8xOz/8h/z5YwfvgvJ/g+M8SFt2u1Gjv2GzNe3st1ygITqA0iyEqtqx3fJVFRYqaiAVA8llG8HZ6PrpcHY3ERQV2VFOGXMA6bxum0q1kN5IMNv6UXc3GRiGbPDzDU/XUNubS4xntfScUgeN20ajunwvTgdKqwiHLsuBhHeFVXHPoBAyjvKh/vep8ornLJtlTjsNjTalspoQs/n+W7LDiLqt7N81x1MH/QpKpWK56ePYubH27n163Reulbm28MF+AsV82YoLdQQXx1FlTaig42EdvZmx44inKgQAhy6GF5++j/Y9VUYpCC+vCKXPzobMXvqCFMFMsmnL6MTriQqIpYjaXtYumM+XjkpJNonEuPlz6Gl25A9OzNp3g34GM9s/ENqRR35tVZ+TikmINxEWU4Nm5KLoRVF3yfIi2tHd2LRmjQGfLwdX4OWhHBvjD56Flhr8bHKrB/V/YyDoZ0KvU7DQ88MRZZlLBYn21+9n1DHMaD1QHNyVSkynnh3K0LXdzQAmo7NYwfJssy6zFJChYoucUpH4v6PvyFOdmLs7cPyeX/Df8cxgsqdeGih6MU7uHLmvIbrP7hrEwAe40/9tXTZ2J7kpW2icL+Bz55ZxYArYxgwslv7f7xKhdY7iOiZ/0Ky1mJ7OZrOO5+g6MCn1CZcT8jwGzEFtjFRvSxDyTFFsaeth+xEkOyg94aYMRA7DmKvAO9Tuzq7UbjkFH2e1U5MkwFIGq0Wj4QnkQrNOPv6Ij+8ghWJDzA+bDZPeZZSmRzN0GvGE983ts08Td56MvzrqcXO3pAYRjplstdm4JNWAYCTarwr8vGtTWC7RoPZ4sBo0HB1t1DKburDi98noVILPvv7QJb/UcTLo+Pw0mvp/uIR6ur86RvZGEPE4XRw9U9Xk1ebx+DQwazUTWUH0NWwiDrtcIwPL8TDs/Xwyx6zX+TguIGEVKlJP7yNLn3HtkijVmmYNfIXFmyZQFTtJn4++AI9o2/mmj9SyB+YgG5PKQ8vVAy3/a0yzz/zGTHVXkiqaiCerGIzSd5VeKM7EbkVq+dx7LoqNBorGotMZ1s/Xps3v1UZR/WZiHlzKVnZgj5zJrE5qQjvGic+V3dot5K3OiTe2JPJ9ztzqC6op0FdqQU+4Z48NaGlkj/Bs8Ni+Wl3LpXHa6hUC7JSypGMajx6+bN8VHc6mAztkuFMEUJgNGowBduJKU5SFFkrZinr/sOACV1U2zbgsuI6/rDauS7YF6OHlpK6KjZIScQB0d8pnlTVXmqy47wJfvhhRo88ad5VjQQOFYN6nH6cyZRbhvNF+gac5Z7sX591Zoq+CSq9ibIr/kPYhrsJsWYScuDfWA+8QcaI1+k89lYlka0OMrcq5pjU9VCVoxwP7g5D7oG48RA1GNRnF9rk/zKXlKKXZZlci42Rfo0DalQqwd3PNZ84442YHwEYD3BZ6y6QmdX13LT4AKWl9TwwLp7LhnUk952FWJwWMp75DQMCT1nmh+nVHNNHN7t21O5klg6IJ0inpkItI+wSshP6BXlzRYdGV656q/Ip3DtYzbFjP7EnfQ1fle2mULIyJGwIn4z/hMWzR9PTo4gOI6/DeMPPpx2YUx0bQsjxWoIj4tpMo1MbmDVsFV+/8ypyUQJLJ+4iXxPPNX7HMI/2ZOMWLao6B8MqatCb46gTTrR2FdlBakLkRLpZfMilE6DDGJhCXN9F+OuiqTRs5Whyd8Ii2p730ulwEHncRI+ou6jdbKfGorhijh/aRsvuxO+yOfjuSD4rDhWQnFGBbHGi0qu5rF8oHQM9GRrpx7TYoNPGrPHQqvnjoUZzxUtbUvl0TSoBf1QTP+3ULpfnA7spAo9iK5aqYgy+zcPnSjU1VG2rQaBBN3hYm3noVQ68Yl5jla6crV/HUksaDIOa7kO4N2Ec3boPxyOi7RgyXWeZSP62nk2v5PBTyE7GX9uXft1bV+B6g445L4zlswe2oT7LcBEnCBt+A/VdhqPx9KVk3yp0G58ietsD1BRtwctZCVnbwWkDrSd0Hg0j5ikmGZ9T1w03p+eSUvTVDid1difhrYSmbQ+SJPHyjkwSs8o5klKGZHGi0qt45YdD9B0QhkdcFtVevkwr20ZSxGBeMpWRrldcxmYcPsxVY6/gX0WlHHM4GLjjKACaY1VogOAQT/z0Gj558z4sNoFerUF2KC3umxb9ijHi+wY5QjFyY0oQa5Lup8eeIjL6hdJ19nPt+g0e4VFAOju/fp0rH36v1TRmi5X331mOR9ZoOugEt/4iKJ9cwAeDr+X7NRtZPSSAq9etYuKOb1l393WM7j+JXsOu4MdPP8V5VMOBmg2EhQzgzucfJzsxn5wKG9WanZSn+mC3S0QP6I4sy5QdycJhthLcL77Ba6eusAxfbSAyygtwgEZFmAcMfGUTPoFGJvYJp1eoF79nV5CcX01JuZn6ejt2swMhg6wSBEaYmNE3gnmDotGfod0Yms8F/NToeI6mlvN7ehlXffgbP/9z+DnFSDodaj+lo7QyP53QkxR97eKVOByRBE6wofJo3vlZcCwHo68ndUXVfLdsIXRSOl5rSSNENYSbe8zg5r7ti7rZIyGWnT7r8KkKxasojDVL97Sp6AEMBj0aXyv1xRpKiyoIDDl7jxNjYBTFxcVkEkla0F1cVfwufinLIDAeBt2pKPYOQ0Bz5pFn3bTNJaXoP00tRL8+n0+Pm7n71kb/7rwaC2GeOuocThYnF3FVXAgBRi3fHCvgpZ+OIiSZkEAPSiot1JeakQEPPz2PTE5g5aYs9lpr2ZNZjmP4+zxzYCu71DLPddRSY+rCyOR9fP33GzGO6QPAuG6hLCks5+2sIoprrVgrlFC0QRoNteUVfFw6hDK5cai0SVQxSHOEGZ1mEu0Xx8FHXqRPZg2wDACbBkb+Z0G7fv+RzUvxXLGNKk9B57HTW03jdDp5/70leGSFERRWR1+zLwAvb/VivTWFF2QD8bXF3PWL0plrP7Seffn1pL32BAW9QimODiV0ty+3v6vMat1xyGw6MpuCzF0s+OJ5tB4yBnUghx9fhh/K/+DY0lQi7hmExtNA7gc78MYfa39B52lDKHjmd8rUEgldAkjNqmTR2jQaupG1KvQ+OnyCPQjzMjCycwBzekcS5HHucYya8u1tg5j83nYO51Uz86NEltzdMrLh+cLDpcCrS3NpGvTXlpxKTUoARu90DGNubXZN8uaDfL/5x4Z9o9ATagnFrncyd+BrTEnoi1rV/hdeSEAgT7x6A3bJzlsP/oRKCGRZPuULbuDkzuz4roANC/cx64GWJsFTYbVaycjIIDU1lbS0NKqrFa+j0NBQkgb/h55dOuHfuc8Z5enmzLikFH28pEIA5ckVjP0ikY7+HiQX1pCfVYXWpEWWZRx1Dl7z0zOiXxjrNmejEgJPfz3Zx6tQqVVMHtmRwehYvPs4ry05ggWZLpIGb6eO3ouWUV+TRD0wUG9m0/ApdOwQiVGvR5Zl7lu1gdFhQczo14epQT70eG0TqlrF3e94SR1egf7cKNbwH/k6AG41/MZzz/0buKHhN4jMFwDIGRaNunNnIi+f0mao4aZsXPgqoc9/hdYg8Pj4DeL7tXwY9x49zNovDuNTG4boWcrAnDAsskz9tTGUbsul2/ZivjQKckQBOtcE0JO3l7PgCRt+WUbGqxOxdHyL3vdORCWam0g8vAPw1YYwxG8anrs0aGVvamLqQafCcNSD/Pd3I6tkTLIv5r5O4maOpqSwFocAbUcv1tw8AIdTYvGxIo5XmRnVwY+BYT5nHD74bFCpVPz8z+EMe3UTe7Ir2JNVzoDotuP4ny2yJOG1610qZC+84hpNM1WfLqAmPQqBwGdWS5PNoQOK59aomEFYrFYGThjKtk2/IcsWrup+9sHGtCotksqJd14Eb73/HQ/eN7vNtP1HJJC4MIeSdAdJO1MwehqI69F69ElZlikpKSEtLY3U1FSys7ORJAmdTkdMTAyjR48mNjYWb+/2zWXs5tw5raIXQhiArYDelX6JLMvPCiGeA+4ASlxJn5BlucVkiEKIK4F3URxVPpNl+ZXzJHsLql1K1eCtIz2tnHRJ+bwN7eBNcYUZZ62DkA7eFOdUs35jFn6BRpbNGUzngEZ/2xW/ZvL/1hzFA0GYTstj42JJXZCJXFVJSKdITP496XX5UG4J8aHH/izmewTz46rtGOw2Sr2DWFIFfvv3cNuiIgB6xwZQ8scf2K16Nv60mc3hd0JuFRGUENWjZadhyD9mUPTxEvqoIeTJD9r1u4tyUwh54SskFYQs+Y6o2JYBx3buT2LPx6WYhD9BU+z0SI9FK+qwdwuk14AIpH5h7NyVg2ZVBkNtMZT0nIbcJYcjna9kcFkdkfdMoMeotkM6+ATE0CtsMN6qAGwDBR2njkatUzrNMlftQL8ZhCSw9peJu240AEd35hKDIHKY4k6pUau4vttf40VRZbZTUa/UnwjfP8dWX1FWTKz1CHv8JjIgrNGF1KpEHybkZl80nZW+FVmWOX4wnXVr15FrLqZHQCxjbprUeI1kxaA6947jK//Rg9/fKsJwJJyfNm7kqrFtt9YD47SUHdOy7ctcZCQqr69h4CjFK8tms5GZmdkQR6aqSplxKzg4mCFDhhAXF0dUVNQ52/ndnB3tadFbgctlWa4VQmiB7UIIl1M0b8uy/EZbFwoh1MAHwDggF9gthFghy/LRcxW8NQqqzKhVgiOPXYFKQFatBbNDopufJ3V2J2VmGxEmPQ9sSEbtlHnlii4YTgp49vP+fBCwfu5IIkJMrP5oHaChU281k++9pyGd0+mkS1UxRSotHk4HegF+VSXEZu/B1u0bQCmWg2lljC/LYkPoAG5LVAa/xHjLLLU+wcH6lrE//Oe+SPFnS6jYnol5TC+MXToQ/O6ShnjvTXHYbWz579N4frMSgxqcL8xtVckDbPnlEJ6EETZF5qpRY8jZtoNqo5Zutyi2WZVKRUKYjix7CRBOTXAU0TdP5qb4ka3m1xr+3TyRUqx0uuZyRJMWf6dJl1HdqwiVUGOKaIxrb8mpwYZMbPdzn2HoXNmaUoLNoYRvmL8jm22pJbz6t950Cz9/rU6/wBB26wbRuTIRh8OJRqOmbtkKbJYoTFFZaLopLpelWYWs/OEnsuoLUMsqegbGM/X2xrhCdoedarmaDqpzj+feN747qnlq1nx0mNzF/nxWuIzbb7ym1bSz5l5OZnI+O9YepfwPFTt+Ssehq25otTudTrRaLZ07d2bEiBHExcXhcw5zCLs5f5xW0ctKeMta167WtbQ35OUgIE2W5QwAIcT3wFXAn6ToLYR46VGrFFtjJ6/GlpmnVo2na3Lu98a33fE0JiGY9cWVfL86jQdv7UNJTh2ybGD8Hc07utRqNVumNx4rKq1g8vw19Dv0O5XiCh7q9yEbSh7nwPEq1oUO4omYcnbZkvg1dyh6ZDyxMDD9PeDpFjKE3DqBqjW/ItkkyjenY5s5goB/3IfximsbFP6R7SsoeO45InLNHI/2xPTss4R2HdymrTU4zkRdLtjKbWT9aydGQDW40Uqcty0J888F+IkQqiIr2LdrNVnzN3Pzi+1X9Cq7Bqe+Dmh5f+/IkBbHdBLUIaM6iw7V883V/SJ5d2MqWWX1fLg5HYDJ/9mGp17DsNgAekX4ctuIThjOIcSxEAIRP56Aw7vIy0nBd38yFbsD0Rsz8J7V2Fpf9t1i8u1lDO/Yn8ETR+AV6tssn7WH11Kvrqd3YJOZkGQJm9OGTbJhc9qwOq1YndaGbZvT1my76XmbZKO+Xzmm7f5Yt/myVXUMp0PC4ZBw2mWcDklZ7Mpa5fAGanHanaxdu5bAwEAGDRpEXFwcHTp0aBH90c1fT7v+I66W+V4gFvhAluWdQoiJwD+FEDcDe4AHZVmuOOnSCOB4k/1cYDCtIIS4E7gToEOHs2upFFRaCPU5t8/ZWRNieX97BguPFtDh5Syqy0x4eFWi1bXuu5ufV8jrCzexqtiIRXhxIKoHPUuruP++lfxTCHZmlLF473Fu/9tEDn8diVOu52g1fKMZRz/vavpIUguXSb+H38HvYcWmW/bILEpWHqL2wZfR+7+KKj6UP+rqiT5UiclTUPLobMbMfoQ3n/gRr5rDmI3VTPl/vejaqTGo2sKfVlO92YjFUMGYiG54HCnF4qkldmInanKLyf42Ee8KH5w48Lspno49OpH89nKydxciOR2o1O17cKUaCWG0tdtrRRNgwK/AQn5BDVERf769VpZlZFlppUiyjOTal2QZhyQTE2Qiq6wegOsHRbF0bx61VgdrjxSx9kgRi/Yc5+kp3XBKEk4JnLKMJMk4TyyyspbkJsdcx5V0UFgUxgAgYMk1WGw3A8NRdxtM5aYKcJbhcDjItyvRFws0lSxdsxyn09lsSZFSwAcWFS/ilwW/YHVasUtth35oD12LLiOYfqhUgtQ9xag1ArVWhVqjalxrVOgMatRaHQ7Jhk+HYIZNux8/v/+tuC//F2nXEyzLshPoI4TwBX4UQvQA/gu8iPLcvAi8CZwcZLu1J77VrwFZlj8BPgElHn175DqZxAzlAUnKrWySb9vpT+gjlRBo1AKNSoVWLZjULZjPDuWzWjrOQHUI4/7ej/rqKoQQOGWYvy2V2ppajmYVsaXCgCR86GKs45GJsQzrr9ix7U4ZIWT6d/RTOvYsFaSmHAWi0WPDs+tY+s66RSmONgbPCJWKwDd+wPum7dSumE/1r4lYdufSSQMpk3sx8vG38Q2MZF/KYbxqFJOI0ezNhlczWB7xO6HxXtTXW5B3BlEXVMD144diTlQmTLC5vnpSfthMSGUYdc5qfOfE4xMfiSQ5CYiMImtHMeVFaQSGN/YlKB94MrIsKbJzYl9GXxyFPaIQydZyiH/jzxMNtcIQ4QWHK0nak09wiCcalQqV4IzdG6vMdqa+t53CagsalWhQ4CeUuCTLipRnUKsW7jre4lhOeT13fHN28yScQIeJe/WBRNbn4xSHEepBWFIqlZe9WmAWjROGnwipq9Fo0Ov1qNVq1Go13ipvLJKFyMhI9Bo9OrUOvbpxfWJbp2p+vOl5rVrbIu2ZeO64+d/ijCceEUI8C9Q1tc0LIaKBlbIs9zgp7RDgOVmWJ7j2HweQZbn1MJIuznbikejHfjl9ojPAEenB3G1vNjv2QfSdSOLcHoid+nsIEZXNDwo1qDTKIlr3NJGRwVzLMb2WmVFhCARqNNye2GY3STOu8m3+VSK0KmS7Ypf+5fgn1DpO/iCD2GlZmMIsnNZaJ6npsuHzdslxMh9gYSG2hn2tWqBWKS9eZa3sq1UCleslIITyghYC8irMOCSZEG8903qHKyYS13mVAIFoeIE0O95kX3PiPmpVw7ZGLVCrlH21ALskEx3giUqckOfEtSpUKpRjQqByXa9y7Z+Q+8Sx0+F0Ot2dlm7OmFNNPHJaRS+ECALssixXCiGMwDrgVWCvLMsFrjRzgcGyLM866VoNkAKMBfKA3cANsiwfOdU9z1bRpxbVkFNe38pvaJm26c92Ssqnu90p4XAqn9vfH02kk7ed2R4+4GohgszuUvg8Q8Pwjl5EBfviafJoMAcoa7khb0mSmx2vrzcTVHuMOR1L0OAAyQGS07Vusn2K/0lWdSY7vPwoD+uBU3YiSRLqo4HE0A1fk3dDg1mSZMorqqkqr8c3wIMAfx+8cmvwsTowJfgDAmQZW2U9+bnJOBLUSLITWZKQnE6cTjt15sNEDw5Ao9OAEAhUuILSo6hP4TouwKlGvTEKoykKfYQvjR9zcvOfIzf8ARnSS+tI8lZRq1e7yl7C4TJ5NK4lZe2UG8wuuFrrJ8pXq1bx8IQu52y6c+Pmf5VzVfS9gK9R3CNVwA+yLL8ghJgP9EF5arOAf8iyXCCECEdxo5zkun4S8I7r+i9kWX6pxU1O4lymEnTjxo2b/4uck6L/K3Arejdu3Lg5M06l6P/8YYdu3Lhx4+Yvxa3o3bhx4+YSx63o3bhx4+YSx63o3bhx4+YSx63o3bhx4+YSx63o3bhx4+YSx63o3bhx4+YS56L0oxdClADZF/CWgUDpBbzf+cAt84XBLfOF439R7otJ5o6yLLca8/uiVPQXGiHEnrYGGlysuGW+MLhlvnD8L8r9vyKz23Tjxo0bN5c4bkXvxo0bN5c4bkWv8MlfLcBZ4Jb5wuCW+cLxvyj3/4TMbhu9Gzdu3FziuFv0bty4cXOJc8kqeiFEHyHEDiHEASHEHiHEoJPOdxBC1AohHmrj+teFEMlCiCQhxI+uaRQRQkQLIcyufA8IIT66iGT2F0KsF0KkutZ+Tc49LoRIE0IcE0JMOF8yn0puIcSgJuV0UAhxdRvXL2qSLksIccB1/IKX9RnI/JwQIq9J2klNzv0pZX0eZL5o6vQZyHzB6/QpZB4nhNgrhDjkWl/exvUXvD6fFmXC5EtvQZkJa6JrexKw+aTzS4HFwENtXD8e0Li2XwVedW1HA4cvUplfAx5zbT/WROZuwEFAD3QC0gH1ny034NGkDMOA4hP7p8jrTeCZv6qs2ysz8Fxr/4c/s6zPg8wXTZ0+A5kveJ0+hcx9gXDXdg8grx15XZD6fLrlkm3Ro8x85e3a9gHyT5wQQkwHMoA2pzSUZXmdLMsO1+4OIPLPEbP5bTkHmYGrUGYDw7We3uT497IsW2VZzgTSgEEtLz9rWpVbluX6JmVo4DQTzwohBHAtsPA8ytYW50XmVvgzy/qcZL6Y6vQZlPNfUafbknm/LMsnnskjgEEIoW8rkwtcn0/NX/F2uRALkADkAMdR5qvt6DruCSQCJtpolbWS18/A7CZv5TpgP7AFGHGxyAxUnrRf4Vq/f0J+1/7nwIw/W27XucEoD0UtcPVp8hkJ7Gmyf8HLur0yu/4PWUAS8AXg92eX9fkq54uhTp9BOV/wOn0qmZukmQFsuFjq8+kWDf/DCCE2AKGtnHoSZULyubIsLxVCXItSEa4AngfelmW5VrQ2a3jLezwJOIDvXIcKgA6yLJcJIfoDy4UQ3WVZrr5YZG7ttq0cO6OW6lnKjSzLO4HuQogE4GshxGpZli1t3OZ6mrd+/oqybq/M/wVeRCnHF1E+0f/OOZb1hSjni6ROn2ndaHHbVo796eXsurY7iulr/Gluc17r8zlxod4oF3oBqmh0HxVAtWt7G0pLLAuoBMqBf7aRxy0oLWmPU9xnMzDgYpAZOAaEubbDgGOu7ceBx5ukWwsM+bPLupV0v7ZVVoAGKAIi/8qyPhOZm6SJxmV7/TPL+jyV80VRp9sr819Rp08lM4q5KwUYdpo8Lmh9Pu1vuhA3+SsW4A9gtGt7LLC3lTTP0bYZ5ErgKBB00vEgXJ0+QGeUTzv/i0Tm12necfWaa7s7zTuuMji/nbGtyu2614kOt44ots7AU5T3lr+6rNsr8wnl49qei2Iv/lPL+jzIfNHU6TOQ+YLX6VPI7Ou659/akccFrc+nledC3OSvWIDhwF7XP2Yn0L+VNM/RRGkCn+F6w6J07hwHDriWj1zH/4ZiVzwI7AOmXkQyBwAbgVTX2r9JuidRPBOO4fIo+LPlBm5yldUBV1lNb01u1/5XwF0n5XvBy7q9MgPzgUMoNvoVNFf8f0pZnweZL5o6fQYyX/A6fQqZn0KxsR9osgRfDPX5dIt7ZKwbN27cXOJcyu6Vbty4ceMGt6J348aNm0set6J348aNm0sct6J348aNm0sct6J348aNm0sct6J348aNm0sct6J348aNm0sct6J348aNm0uc/w99zh3yjgSe2AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  251761.0 people (VAP of 206374.0 ) and  137636.11011111233 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for SE corner\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-76.5,36.6)\n",
    "pt2 = Point(-76.5,34.5)\n",
    "pt3 = Point(-75,34.5)\n",
    "pt4 = Point(-75,36.6)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  47 tracts w pop 137279.0  to reassign to tracts...\n",
      "[207, 209, 315, 401, 402, 428, 441, 1369, 1389, 2531, 2637, 2659]\n",
      "I have flagged  232 precincts to reassign to precincts...\n",
      "[1171, 1584, 2430, 5002, 5003, 5005, 5006, 5007, 5208, 5211, 6120, 6121, 6896, 6918, 6922, 6923, 6924, 6926, 11231, 11232, 11233, 11625, 11766, 11769, 11864, 11865, 11866, 11867, 11868, 11869, 11870, 11881, 11882, 11883, 11909, 11910, 11911, 11915, 11917, 11938, 11939, 11990, 11991, 12006, 12007, 12008, 12009, 12374]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.15:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedPrecinct[p] = 1\n",
    "            if cutPrecinctList == [-99999]:\n",
    "                cutPrecinctList = [p]\n",
    "            else:\n",
    "                cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  137279.0 people (VAP of 109895.0 ) and  78822.61742583403 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n"
     ]
    },
    {
     "data": {
      "image/png": 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IQmAOvqPzN/yFfamIeIFkoOHh+C6gX4PnfYGC40ocgD7do4GWz4Jf+dQynHbB5TGM7pPAH88fzcjeWuSVUq1X168+GAt9IL1uUvxH8ohINHAKsB54C5jlnz8EiAAa325lGTBYRDJFJAK4GHinvcI3Z3Oh75TC8h2HKK12HbX8u+P78sj3xzEpIwmXx3DVCZm8e/N0LfJKqTaraxJu2EwcLAI5ok8DnvH3oLEBrxhj5vsL95MishqoBX5ojDEi0hv4tzHmDGOMW0RuAj4E7MCTxpg1HfRZ6o3sncB54/rw5ordjLn3I+bfPP2I5pgIh42zxvTmzNFp7Cyqoq//G4BSSrXVql0lJMY465uGg0lYD4GQced79dPb/njmcb+eUko1xeXxcsIfP2NM30T+/cMmRyHocMcaAiH4vmO0ozNG9wIgJsK68SeUUuFvX2k1hWU1TBmQZHWUJoV1oX/s0glcMqk/lbUeXlq6g2D89qKUCn29E6JJiHaycV95yytbIKwLPcD3/FfB3vnGKt5audviNEqpcGSzCZMzk/hk3T7KmugAYrWwL/QT0rvz9V2zAFi9u9TiNEqpcHX19EwOVtSyYMPxXfDZEcK+0AM4bL6PmZYQZXESpVS4Kizzda9MjA6+O8p1iUJfNxxxMPZvVUqFh/9+vY30HjFMH3T01fhWC/vK905eAVP+8CkAQ1ObvtGIUkodj7UFpSzbdojLp6RjC8KxbsK+0P/q7dUADOsVx6TM4Oz6pJQKbc9+s40op40LJvRreWULhHWhN8ZwqNLFxIzufHDLiYgE355WKRXaSipdvLWigHOz+pAQE3zt8xDmhb7GP5ocoEVeKdUhFm0+QJXLwwXZwXk0D2Fe6KOcdiZmdGdjYXBexKCUCn21/gPK7kF6NA+tHKY4lJRVu3h0wWaWbTsEQFWth2gdCkEp1c6M/xYbwdxqEHaF/j9fbWVnUSUvLN1BrdvLiLR4fjgtXYu8UqpD1I2sErxlPswK/Z6SKn4zfy0A/ZNi+OmpQzgnq3dQ72mVUqqjhVWhv/XllQC8/qNpfP7tfn49fy0/eXklS+6eTWq8XhWrlArcf7/exi/fXkO0047dJtjEd2/Y7jER/oeTxJgIFm4oBCCYjyfDqtBHOX3NM5f++xuqXYd73CzcUMhFE/tbFUspFYIS/EMZ1Hq8XDE5A4/XUFxZy6FKF8WVtWw5UE5xhYuyGjfRTjuJ0REWJ25eWBX6By4Yy8OfbSJ/VzEXT+zP7OE9mfDbT44o+kopFYizx/bm9eW7WbHjEDfPGkRiTNOFvNbtxWtM/YFmMAqr7pU9ukVy79kjeeOGE7hwYj8Sop1EOmx8ur6wyXvHKqVUc0SE208bQlm1m3fyCppdL8JhC+oiD2FW6Btz2G0MT4vni2/3s3RLkdVxlFIhpu7udHFRod34EdaFHuDM0WmA78YjSinVGgnREThsQv6uEqujHJewL/SnjkgF4EB5jcVJlFKhJiUukvPG9eG5b7aTv6vY6jhtFvaF/t531wDw14vGWpxEKRWK7pw7jJ5xUZz9yCL+/OF6XJ7Q69wR1oW+qtbDQv9tvW59OY9THvzc4kRKqVDTo1skz10zGYBHF2wm+7efUFRRa3Gq1gnrQh/ltHH19Eyi/WfE7cF8RYNSKmhlJsfy3o+nA1BS5WL8bz5mUwgNlhjWhV5EuOesEaz7zRwmZSZRUqVdLJVSbTOydwJf3DGTzORYAC779xIKiqssThWYsC70DS3ffoi9pdVWx1BKhbD+PWJYcPvJ/P3iLPaWVrN8xyGrIwWkxUIvIlEislRE8kRkjYjc559/r4jsFpGV/scZzWy/TURW+dfJae8PEIhl24pwew23njLEirdXSoWwiho3y7YVUVhajdvjxeM1LNp0gAiHjSkDelgdLyCBXAVQA8wyxpSLiBP4SkQ+8C/7qzHmLwG8xkxjzIE2pzwO1S4Pf/pgPSlxkVw7I9OKCEqpEFXt8nDmQ1+y7WDlUcu+P7k/yd0iLUjVei0WemOMAerOOjj9D9ORodrTna/nk7P9EL89dxQxEaF9dZtSqnPtLKpk28FKLpvSn4Ep3SirdmMMJEQ7uHBi8N46sLGAKp+I2IFcYBDwqDFmiYjMBW4SkR8AOcBtxpimGqwM8JGIGOCfxpgnmnmPecA8gP7922ekyUMVtby3ag8XZffjsinp7fKaSqmuo3diNAnRTt5ftZfXrp/KgJRuVkdqk4BOxhpjPMaYLKAvMElERgGPAwOBLGAP8EAzm59gjBkPzAVuFJEZzbzHE8aYbGNMdkpKSus+RTN2FFXi8hhmD+/ZLq+nlOpaYiMdPH7peIoqalm6NXTHy2pVrxtjTDGwEJhjjNnn3wF4gX8Bk5rZpsD/sxB4s7n1OkJynK/97MWlOzrrLZVSYSbePy5999jgHW++JYH0ukkRkUT/dDRwCrBeRNIarHYesLqJbWNFJK5uGjitqfU6Spr/rlILNuynTIcpVkq1Qan/+hunPXQvuAzkiD4NWCAi+cAy4GNjzHzgfn+3yXxgJnArgIj0FpH3/dum4uulkwcsBd4zxvyv3T9FM8pr3fXTwT5etFIqOI3tl0hCtJOnFm3DmJDph3KEQHrd5APjmph/eTPrFwBn+Ke3AJaNJrZ+TxkAP5iajtPeZa4NU0q1o9hIBzfNHMTv3l/Huj1ljOgdb3WkVgvr6ped3p3x/ROZn78n5AYhUkoFjxlDfB1ENuwrtThJ24R1obfZhD98dwxl1S5++95aq+MopUJUZnIsaQlR/Pfr7SHZfBPWhR5gaK84rj9pIG8s381XGy25OFcpFeIiHDZ+PHswK3YU1w99HkrCvtAD3DhzEJnJsdz95iqqaj1Wx1FKhaDvTehLcrdIXgjB7tpdotBHOe38/rzR7Ciq5P4P11sdRykVgpx2G6P6xLO2IPTa6btEoQeYOrAHV0zL4KlF21i0SZtwlFKtd7C8lsQYJ6Uhdl1Olyn0AP83ZxgDUmK5/dU8vQmJUqrVBvfsxpqCUrLu+4jfv7/O6jgB61KFPjrCzl8vzGJPSTVPfLHZ6jhKqRDz5wvG8sI1kxnRO56Xl+0MmR44XarQA4zpmwDAjqLQuAWYUip42G3CtEHJXDY5nZIqF/m7SqyOFJAuVeg37y/n759uBGB4WpzFaZRSoeq0kb0A+HTdPouTBKZL3InD4zW8nruLn72eXz9v9rBUCxMppUKZ2+sFwBsaLTddo9Cf9tfP2by/AoDx/RN58oqJJMaE7pCjSinr7C2pZtYDCwGYM6qXtWECFPaF3u3x1hf5F66dzLSByRYnUkqFstzth6is9XBRdj9G9UmwOk5Awr6N/kfPL6+fbm2Rf3TBJm57JS9kzqwrpTqW12tYsvUgACZ0bp0d3kf01S4PH6/1nSzJTI5t1bY3PJ/L+6v2AjC6TzwnDkmhxuXlH59vxmMMPWIjSIqNIDHaSVyUk7goB/HR/p/+53FRTuy20L1ZgVLqSGv3lPLfr7eT3C2C7PQkq+MELKwL/fur9tRPv3b91BbXN8bg8hi2HCivL/IA97575MiX6T1iKK50BXTRVUyEndhIB90iHcRG2omNqJt2+Oc3XO77WbfTiPfvNOKjnUQ6bIjoTkMpK327z3ePi/9eNTmkxqUP60L/9soCAEb3SaBHt8gm16lxeyipdFFYVsNZD391xLL//eRErns2l+0HKzlzdBpxUQ5uOWUwaQnRALg8Xsqq3ZRVuyirdlNa5aK04fNqF+XVbipq3ZTXeKiocVNe42ZvaXX9dHmNm2qXt8XP4rRL/TcF307g8LeH+Gjft4d4/7eIup1EnH9ZfLSTuEiH7iiUOk7Lth0iLsrB0F6h1T07rAv9bacN4fNv97PtQAX/+WorZ4/tjcHQM853L9nKWjcjfvlhk9uO7ZvAsF7xfHzrSdht0mQTjNNuI8nfhHM83B4vFbWHdwRl1b4dRsMdR2nV4fm+5y72llbXL6tyHXtUTodN6B4bQVJMBN1jnfSIjaR7rNP/3PcZ4qOd9IqPYnha6BypKNWZ8nYWk9UvMeSaZMO60I/pm8jC20/m52+t4jfz1/Kb+b4mmMum9Cch2smjCw4Pg3Di4GTOGpPGnFFpbN5fzgh/sYtwdPz5aofdRkK0jQT/3ebbou7bRWnV4W8TdTuBkioXhyprOVRZS1GF77F+bymHKn3zG59rXnL3bFL9N1ZXSh22sbCMSZnpVsdotbAu9AAZybE8d/Vk8neVsGxbEb99bx3PfXN4POkzR6fx0CXjjthDj+/f3Yqox6Wt3y48XkNplYt9ZdXM+duX9IyLpGdc081cSnV1o/skMD+/gFtPGUJCTNsPzDpb2HevBBARxvZL5JoTB3DrKUNIio3gw5/MYOsfzuDRS8eH3New9mT3N+kM6xXPuVm9Kfafr6izp6SKihq3hQmVCh6/PmcURRW1IXdfiy5R6Bu65ZTBLL/nVIb2itOTk424vIZaj5ddh3wDvn22fh9T//AZVz29TK8lUAoY1SeB88b15YWlO9jo74ETCrpcoVdNKyyt5r38PZw+MpVx/RJ5Y/kurv1vLgBLthbxXoOuqkp1Va/n7uKdvN0MSI4luZmefMFIC70CIM8/3Oq8GQN4avE2fvpKHpMzk8j71WmMSIvnd++t0/vtqi7L6zX85cMN3PZqHpMyk3jjhhPofpy97RpbvbuEytqOaSbVQq8AX68dgHveWsNv5q9lzshePHnFRBKindx79kj2lFTzry+3WJxSqc5X7fJw80sreGTBJi6e2I+nr5x0XD3kGqtxe/i/1/I56+GvOP/xr9vtdRsK+143KjAzh/Yk0mFj7Z5SLpnUj9+eO7r+JPWkzCTOGN2Lxxdu5qKJ/bTrpeoy9pfVcO1/c8jbVczdZwzj2hMHtOu5vbJqF1c9vYxl2w4BsG5Px9x4vMUjehGJEpGlIpInImtE5D7//HtFZLeIrPQ/zmhm+zkiskFENonIne39AVT7iI6w88K1k/ndeaP4/Xmjj+qJdOec4Xj8X1+V6go27C3j3EcXsX5vKY9fOoF5Mwa2a5GvrHVz1dPLWLGjmIcvGcfFE/uRGt8x7f6BNN3UALOMMWOBLGCOiEzxL/urMSbL/3i/8YYiYgceBeYCI4BLRGRE+0RX7W1CehKXTk5v8pe5f48Yrjwhg9eW72L17tC4fZpSbbVwQyHnP74Yl8fLq9dNa/dx5z1ew80vrCB3+yH+fvE4zhqTxpYDFQgd0xOwxUJvfMr9T53+R6B97SYBm4wxW4wxtcBLwDltSqosd+OsQSTFRPCb+Wu1u6UKW899s52rnl5Gv6QY3r7pBEb3bf8x53/73lo+XV/IfWeP5IzRvfj1/LUs3VpETKS93d8LAjwZKyJ2EVkJFAIfG2OW+BfdJCL5IvKkiDR1OWkfYGeD57v885p6j3kikiMiOfv37w/8E6hOEx/l5CenDmHJ1qL64Z+VCicPf7qRX7y1mpOH9uS166fWD2DYnp5fsp2nFm3jyhMyuHxqBo8t3MxTi7Zx/vi+/OeHE9v9/SDAQm+M8RhjsoC+wCQRGQU8DgzE15yzB3igiU2b+h7S5KGgMeYJY0y2MSY7JSUlkFjKApdM7Megnt34wwfrqXW3POqmUqFkfr7vepFffWcEsZHt31flmy0H+dXbazhpSAq/OHMEb6/czZ8/3MA5Wb358/fGtPq+GYFqVfdKY0wxsBCYY4zZ598BeIF/4WumaWwX0K/B875AQduiqmDgsNv4+ZnD2Xqggue+2W51HKXa1cPfH0dclIOrn8mhpLLl+020xp6SKm58fjn9e8Tw8PfHkbv9EHe8ms+kzCTu/94YbB04FEsgvW5SRCTRPx0NnAKsF5G0BqudB6xuYvNlwGARyRSRCOBi4J3jTq0sdfKQFE4cnMzfP91IcWWt1XGUajdDUuN44vJsth+s4O63VrXb69a4PVz/3HKqXR6euHwCB8pqmPdsDn2Tonni8glEOjqmbb5OIEf0acACEcnHV7g/NsbMB+4XkVX++TOBWwFEpLeIvA9gjHEDNwEfAuuAV4wxazrgc6hOJCLccPIgSqpcLNlaZHUcpdrV1IE9uGJaBu/l72m3A5l731lL3s5iHrhwLN1jIrjy6WXYRHjqiokkxrTvFbZNabERyhiTD4xrYv7lzaxfAJzR4Pn7wFFdL1V4iOuAdkylrFbj9hLltBEfdfxXwL68bAcvLt3Bj04eyOzhqVz67yXsKanmxWunkN6jY9rkG9MhEFSb1B3pdMbRiFKdLdJho9rl5bS/fUFRRduP6vN2FnPP22uYPiiZ208byu/fX8fSrUXcf/4YJqR33n0vtNCrNinyF/rusaFz8wWlAnXX3OHcNHMQmwrL+WRd27oSFxRXMe/ZHFK6RfLQJeN4N6+gvlvlueOa7GXeYbTQqzYp9vdI6K5H9CoM2WzC2Vm9AYh2tv5EaUmViyueWkpljYf/XJHN3pJq7nwjn0kZSdx9xvD2jtsibWBVbXKoopYop42oNvwRKBUKSqt8BzPxrRypstbt5fpnc9l6oIJnrpxEWnw033nkKxKinTxy6Tic9s4/vtZCr9rkUKVLj+ZVWCut9hf6qNaVyYc/28jXWw7y4IVjyc5I4oqnlrKnpIqX5k2lZ5w1I79qoVdtUlxZy56SavJ3FWMTwW47/HDabNjtgtMmOO02Ihw2nHYbTrvo7RtVyCir9t0EZEdRJeP6B3bidPXuEh5buJnvju/Dd8f35RdvrWLx5oM8cMHYTj352pgWetUmbq9vJIuzH1nUqu0i6gu/1O8AIhy2BvN90876eeJ7Xrde43Ub7ETsNsFhE+w2m/+nHP5pb3q+025jaK84S75Oq+BWN1T3twHeG7bW7eWO1/JJio3gl2eN4H+r9/LcNzuYN2MA50/o25FRW6SFXrXJn783hjUFpXi8Bo8xeP0/PV6D22Nwe724PAaXx4vL46XW7aXWY6h1H35e97PG48Xl9lLbYN3KKk+T69Y2+NleA2hOzkziuWsma7FXRzhzdBr3xKxm2dZDGGNa/Db6+MLNrNtTyhOXT6DK5eHON/IZ0zeB208b2kmJm6eFXrVJz/goelp8pym3x7czqXV7cXu9vp2M1zT46cXt3/Ecucxbv0Nat7eU+/+3gT9+sJ57ztJbJajDRITLp6Tz0GebOP/xxdxz1gjG9k3kl++sJi0hmhtnDqpfd/3eUh5ZsJGzx/Zm9vBULvv3EmrdXv5+8TgiHNYfQGihVyHLYbfhsPvujtVWM4f1ZF9JNf/5aitj+yVy9tje7ZhQhbrrThpIpNPOPz/fzHmPLWZASixb9lcAcPHEfvToFokxhl++vYa4KN/9lf/79Ta+3nKQ+8/vuNEoW8v6XY1SFvv5mSPITu/O/72Wz4a9gbXHqq4hNtLBjTMHsfiu2VwxLaO+yAPsL68B4MM1e1m6tYjbThuCy+PlgY++5aQhKVyQbW27fENa6FWXF+Gw8dil4+kW5eD653Lru9UpVadbpIN7zx7J6z+ayt8uygLg/VV7qXF7+P376xmaGsdF2f349fy1uDxefn3OyKDqYaaFXil85xweu3Q8O4sq+enLeXi9eqtEdbQJ6UmcPbY3fRKjWbLlIM8s3saOokp+cdZwFm8+yHv5e7hx5qBOG6wsUFrolfKbmJHEz88czifr9vHYwk1Wx1FBoqC4ihL/VbIrdhxi4M/fZ3dxFaeOSOXhTzcxa1hPJmYk8cu3V5OZHMt1Jw2wOPHR9GSsUg1cMS2DlTuLeeDjbxndN5GThuhtLbui3O1FrCkoJWfbId7NL6BbhIM7zxhGebUbY+DEwcls3l9BlcvD3WcM519fbGHbwUqeu3pyh99EpC200CvVgIjwh++OZsPeMm55aQXv3jSdfkkxVsdSnaja5eH8x7+uf37BhL6s2l3Cz988fBO9X5w5grl//4IfTM2ge4yTf3y+mdNHpjJ9cLIVkVukTTdKNRIT4eAfl03A4zVc/1wu1S6P1ZFUJ4py2vm/OcPqnx8or+F3543m1eunctqIVK49MZMHP95At0gHt8wezMOfbaLK5eGO04cd41WtpYVeqSZkJMfyt4uyWFNQyi/eWo1pr8twVVAxxlBS5WJTYTmrd5fUt8X/6OSBfHrbSfx41iDyd5Vw/uOLeWnpTv52cRaXTUnno7X7+OG0DMpr3Dy/ZDsXTezHoJ7dLP40zdOmG6WaMXt4Kj+ePZiHPt1I78RofnrqEKsjqeNgjGFnURW5O4rI3X6I3O3FbNlfTo3be8R6/ZNiuHPuMOaM7MVPTxvKdScN5PGFm3lkwSainL6xlRw24bIp6fzxg/XYbcIts4P7d0MLvVLH8BN/oX/o040kxTi54oRMqyOpANW6vazaXULu9sOF/YD/IqdukQ7G9U9k+qB0UuOjSImLJMJuY3tRJW+vLOCG55fTPymGiyf146Lsflw1PZNHFmzCawyv5uzkrDG9EeCdvAKunJZBrwRrhwNpiRZ6pY7BZhO+uGMmM/68gAc//pYzx/QmJS7S6ljqGAqKq3jum+28tGxn/f1e+yfFMGNwMuPTuzMhvTtDUuPqR6ds7Orpmby/ag8vLNnB/f/bwAer9vL0lRMB+Gx9IRW1Hq46IZM3VuzG4zV8f3L/TvtsbaWFXqkW9O8Rwwe3nMh5jy3ixueX8/y1OtJlMNqyv5w/f7iBj9buwxjDKcNTOW9cHyZkdG/VDT+cdhvnZPXhnKw+3P3mKt7NKyAxJoJhveJYv7eM9B4xjO6bwJ1v5JPVL5EBKcHbNl9Hf1uVCsDwtHj+dP4Ylm4r4g/vr7c6jmpg0aYDnPfYImY/+DlfbTrANSdm8vkdM3niB9nMHZ12XHd1mjE4mbJqNx+u2ctV/ma77Qcr2XqggjUFpZw1Jq29PkaH0iN6pQJ0TlYfVuwo5slFW5mU2Z05o0LjjzxcVbs8/PGD9Ty9eBvpPWL48azBXDKpf7u2l586ohdpCVG8vXI39549sn7+J2v3ATBnVK92e6+OpIVeqVa4+4zhrNhZzB2v5TM8LT7oxjTpKlbvLuGWl1aweX8FV0zL4M65wzrkRvV2mzBzWE/eXL6bGKeDd246gYLiKp5ZvJ2BKbH07R4aF9Np041SrRDhsPHIJeMQ4MYXluvFVBb4auMBLvjH11TUeHju6snce/bIDinydS6fkk6tx8stL6/AJkLu9kN8veUg35vQr8Pes721WOhFJEpElopInoisEZH7Gi2/XUSMiDR57a+IbBORVSKyUkRy2iu4UlbplxTDAxdmsXp3Kb97b53VcbqUBesLuerpZaT3iOHdm6d3ypADw9Piue/skXy58QBnPfwV//pyKz+Yms41J4ZOV9tAmm5qgFnGmHIRcQJficgHxphvRKQfcCqwo4XXmGmMOXC8YZUKFqf6L4X/15dbmTwgibPG6J2pOtona/dxw/PLGdKrG89eNZnusRGd9t6XTUnn5KEp5O0soU/3aLL6JXbae7eHFgu98V37Xe5/6vQ/6q4H/yvwM+DtDkmnVBD72Zxh5Gw/xJ2vr2JU7wQyguS2ceHowzV7uemF5QxPi+fZqyaTEOPs9Ax9u8eETJt8YwG10YuIXURWAoXAx8aYJSJyNrDbGJPXwuYG+EhEckVk3jHeY56I5IhIzv79+wPNr5RlnHYbD18yDrtNtL2+g9S6vfz7yy3c+PxyRvZO4NmrrSnyoS6gQm+M8RhjsoC+wCQRGQP8HPhlAJufYIwZD8wFbhSRGc28xxPGmGxjTHZKio4BrkJD3+4xPHDBWNYUaHt9e1u+4xBz/v4Fv31vHScOTubZqyeREK1Fvi1a1evGGFMMLATOATKBPBHZhm8HsFxEjupUaowp8P8sBN4EJh1XYqWCzCkjUpk3YwDPfrOdd/MKrI4T8jxew6MLNnHhP76m1u3lySuyefKKicRFaZFvqxbb6EUkBXAZY4pFJBo4BfiTMaZng3W2AdmNT7iKSCxgM8aU+adPA37dnh9AqWBwx+lDydlWxF1vrGJUnwQytb2+TfaWVHPLSytYsrWIM8ek8ftzR2tTTTsI5Ig+DVggIvnAMnxt9PObW1lEeovI+/6nqfh66eQBS4H3jDH/O97QSgUbp93Gw98fj8Mu3Pi8tte3xdqCUs59dBGrdpfwlwvG8sgl47TItxMJxhsqZGdnm5wc7XKvQs+n6/Zx9TM5XDq5P787b7TVcULGgvWF3PTCcuKinDx5xURG9I63OlLIEZFcY0x2U8v0ylil2tHs4alcN2MAzy/ZwTvaXh+QZ7/ZztXPLCMjOZa3bjxBi3wH0EKvVDu7/fShTEjvzl2v57P1QIXVcYLaows2cc9bqzl5aE9euW5q0N/AI1RpoVeqndX1r3c6bNyg7fXNevbrbfz5ww2ck9WbJy6fQGykjrHYUbTQK9UBeidG89cLs1i3p5Rfz19rdZyg89XGA9z77lpmD+vJgxdm4dAbuXQo/ddVqoPMHNaT608ayAtLdvD2yt1WxwkaW/aXc8PzuQxK6cbf/VcWq46lhV6pDnTbaUPITu/O3W+sYsv+8pY3CHMlVS6ueSYHh93Gv3+YTTdtrukUWuiV6kC+/vXjiND2ejxew+2v5rGjqJLHLx1Pv6TQHCAsFGmhV6qDpSVE8+BFWazfW8Z973bN9npjDL94axUfr93HXWcMZ/KAHlZH6lK00CvVCWYO7cmPTh7Ii0u7Znv9O3kFvLh0J9efNJCrp4fODTvChRZ6pTrJbacebq/f3IXa64sra7nv3bVk9UvkjtOHWh2nS9JCr1Qncfjb6yOd9i4zHk61y8Ndb6yiuLKWP3x3tPawsYgWeqU6UVpCNA9eONbfXr/G6jgd7r531/DB6r3cNXc4w9N0aAOraKFXqpOdPLQnN5w8kBeX7uStFeHbXr/9YAWv5Ozih1PTuXbGAKvjdGla6JWywE9PHcKkjCTufnMVmwrDs73+oU834bAJN84cZHWULk8LvVIWcNhtPHTJOKL87fVVteHVXl9YWs1bK3dz6eR0esbrQGVW00KvlEV6JUTx14uy2LAv/NrrX83dhcdr+P7kflZHUWihV8pSJw1J4caZA3lp2U7eXLHL6jjtotrl4Z+fb+bkoSkM6hlndRyFFnqlLHfrKUOYlJnE3W+sZlNhmdVxjttn6wsprXbzw6kZVkdRflrolbKYwz9+fUyEnRufXxHS7fW7i6u48/V8hqbGMXWgDnMQLLTQKxUEUuN97fXfFpbxq3dWWx2nzf7z5VYqaz088YMJRDntVsdRflrolQoSM4akcNPMQbySs4vXc0Ovvd7t8fLGil3MHZ1Geo9Yq+OoBrTQKxVEbpk9mMmZSfzirdBrr1+xs5jiShdzR/WyOopqRAu9UkGkrn99bKSdG55fTmWt2+pIAft0XSEOmzB9cLLVUVQjWuiVCjKp8VH87aJxbCws55dvh07/+s/W72NSZhLxUU6ro6hGtNArFYSmD07m5lmDeS13F6/m7LQ6Tot2FlXy7b5yZg3raXUU1QQt9EoFqVtmD2bqgB7c8/ZqNuwN7vb6z9YXAmihD1ItFnoRiRKRpSKSJyJrROS+RstvFxEjIk02zInIHBHZICKbROTO9gquVLiz24S/X5JFt0gnNzyfS0VN8LbXf7R2L4N6dmNASjero6gmBHJEXwPMMsaMBbKAOSIyBUBE+gGnAjua2lBE7MCjwFxgBHCJiIxoh9xKdQk946J46OIsth6o4BdvrcYYY3Wko7g8XpZvL2b6ID0JG6xaLPTGp24cVaf/Uffb9lfgZw2eNzYJ2GSM2WKMqQVeAs45vshKdS3TBiVzy+whvLliNy8vC772+nV7SqlyeZiQ3t3qKKoZAbXRi4hdRFYChcDHxpglInI2sNsYk3eMTfsADX8zd/nnNfUe80QkR0Ry9u/fH1h6pbqIm2YNYvqgZH71zhrW7Sm1Os4RcrYdAiA7Qwt9sAqo0BtjPMaYLKAvMElExgA/B37ZwqZN3SCyyaN/Y8wTxphsY0x2SkpKILGU6jLsNuFvF2eREO3kxueXUx5E7fW52w/RJzGatIRoq6OoZrSq140xphhYiK/5JRPIE5Ft+HYAy0Wk8SVxu4CGA1L3BQramFWpLi25WyQPXTKObQcruOuNVUHRXm+MIWd7kTbbBLlAet2kiEiifzoaOAVYYYzpaYzJMMZk4Cvo440xexttvgwYLCKZIhIBXAy8054fQKmuZMqAHvz01CG8m1fA80ua7APRqQpKqtlXWqPNNkEukCP6NGCBiOTjK9wfG2PmN7eyiPQWkfcBjDFu4CbgQ2Ad8IoxJnQu9VMqCN1w8iBmDEnh1/PXsnp3iaVZlmw5CMCYvomW5lDHJsHw9a+x7Oxsk5OTY3UMpYLWwfIaznzoKyKdNubfPJ04i4Yd+MGTS9myv5wvfzYTkaZOyanOIiK5xpjsppbplbFKhaAe3SJ5+Pvj2HWoijtft6a9/kB5DV9t3M85Wb21yAc5LfRKhaiJGUncftpQ3lu1x5L2+i++3Y/XwJyRaZ3+3qp1HFYHUEq13XUzBrB48wF+M38tkzOTGJx67JtxG2PYU1JN3s5iNhWWYwC311BR46bW7SU+2sHAlG5kpyfRv0fMMV9r6dYiEmOcjOwd346fSHUELfRKhTCbTXjgwrHM/duX/Pillbx5w7SjbuG3r7Sat1fuZunWQ+TtKmZ/Wc1RrxMTYSfCYaO82o3b62sGGtk7nrPG9GbejAHYbUc3zWw/WElGj1hsTSxTwUULvVIhrmdcFH++YAxXPZ3D/f/bwC+/4xtOKm9nMY8v3MzH6/bh8RoGpMRy4qBkxvZLZGy/RIb1isNhE2wi9cXa5fGy9UAFn2/Yz6u5O/nT/9bTOzGKc7KOvqDdYRdMbfB15lBH00KvVBiYNSyVK6Zl8OSirTjtwpqCUr7adID4KAfXnJjJRdn9AhpZ0mm3MSQ1jiGpcVw1PZNTHvycBz/+lumDkunRLfKIdRNjIth1qKqjPpJqR3oyVqkwcefcYQxNjeOfX2xh/d4y7po7jEV3zuKuucPbNHyw3Sbc/70x7Cmp5rzHFrNlf/kRyx02we31tld81YH0iF6pMBHltPPklRPJ2VbE6SN7HdVW3xYTM5J4ad4Uzn98MY8t3MxfLhhbv8xpF2pcWuhDgRZ6pcJIn8Ro+jTRnn48xvfvTnZ6d7YeqDhi/oZ95aQlRLXre6mOoU03SqkWTRnQg+U7DrF0axEAr+bsJG9nMeeOa9+diuoYekSvlGrRD6Zm8G5eARc/8TWp8VHsKalmUmYSl01JtzqaCoAWeqVUi1LiInn7pun858st7DxUxeg+CVw6pT9OuzYKhAIt9EqpgCREO/npaUOtjqHaQHfHSikV5rTQK6VUmNNCr5RSYU4LvVJKhTkt9EopFea00CulVJjTQq+UUmFOC71SSoU5seKmwi0Rkf3A9mOskgwc6KQ47SHU8kLoZQ61vBB6mUMtL4Re5uPJm26MSWlqQVAW+paISI4xJtvqHIEKtbwQeplDLS+EXuZQywuhl7mj8mrTjVJKhTkt9EopFeZCtdA/YXWAVgq1vBB6mUMtL4Re5lDLC6GXuUPyhmQbvVJKqcCF6hG9UkqpAGmhV0qpMBdShV5EskTkGxFZKSI5IjLJP3+Sf95KEckTkfOszgrHzHuqiOSKyCr/z1lWZ61zjMw9RGSBiJSLyCNW56zTXF7/srtEZJOIbBCR063M2ZCIvNzg93WbiKz0z48Qkaf8vxd5InKypUH9jpHXKSLP+POuE5G7LI4KHDPvpQ3mrxQRr4hkWZvWp7nM/mVjRORrEVnj/7du/R3ZjTEh8wA+Aub6p88AFvqnYwCHfzoNKKx7HqR5xwG9/dOjgN1WZw0gcywwHbgeeMTqnAHkHQHkAZFAJrAZsFudt4n8DwC/9E/fCDzln+4J5AI2qzMeI+/3gZf80zHANiDD6ozN5W00fzSwxep8AfwbO4B8YKz/eY+2/B6H1BE9YIB4/3QCUABgjKk0xrj986P86wWD5vKuMMYU+OevAaJEJNKCfE1pLnOFMeYroNqqYM1oMi9wDr4iVGOM2QpsAiY1sb1lRESAC4EX/bNGAJ8CGGMKgWIgaC72aSKvAWJFxAFEA7VAqUXxjtJE3oYuaWa+pZrIfBqQb4zJAzDGHDTGeFr7uqF2z9ifAB+KyF/wNTtNq1sgIpOBJ4F04PIGhd9KP6GZvA2cD6wwxtR0ZrBj+AktZw4mP6HpvH2Abxqst8s/L5icCOwzxmz0P88DzhGRl4B+wAT/z6UW5Wuscd7X8O1Q9+A7or/VGFNkVbgmNM7b0EX4sgebxpmHAEZEPgRS8B283N/aFw26Qi8inwC9mlj0c2A2vl+m10XkQuA/wCkAxpglwEgRGQ48IyIfGGM6/OizrXn9244E/oRvr91pjiezFdqYV5pYv9O+6R0rszHmbf9046PKJ4HhQA6+sZ4WA51ywNLGvJMAD9Ab6A58KSKfGGO2dGhY2py3btvJQKUxZnUHRjxKGzM78DWZTgQqgU9FJNcY82mr3tzq9qhWtl2VcLjvvwClzay3AMgO5rxAX+Bb4ASrc7bm3xi4guBqo28yL3AXcFeD9T4Eplqdt0EeB7AP6HuMdRYDI6zO2lxe4FF8357rnj8JXGh11pb+fYG/AndbnTHAf+OLgacbPL8HuKO1rx1qbfQFwEn+6VnARgARyfS3EyIi6cBQfCeGrNZc3kTgPXyFaJE10ZrVZOYg1lzed4CLRSRSRDKBwQRPEwj4vnWsN8bsqpshIjEiEuufPhVwG2PWWhWwkaPyAjuAWeITC0wB1luS7mhN5UVEbMAFwEuWpDq2pjJ/CIzx/2448P2ut/p3IuiablpwLfB3/weuBub5508H7hQRF+AFbjDGBMPQpM3lvQkYBNwjIvf4551mfCfgrNZcZkRkG74TnxEici6+zFYXoibzGmPWiMgr+P4o3MCNpg0nsTrQxRzdrNAT3/kGL7AbuLzTUzWvqbyPAk8Bq/F9m3rKGJPf2cGa0VRegBnALtMJzUttcFRmY8whEXkQWIav6fF9Y8x7rX1hHQJBKaXCXKg13SillGolLfRKKRXmtNArpVSY00KvlFJhTgu9UkqFOS30SikV5rTQK6VUmPt//Mitk7JKlW0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "bafedc65-1410-41d4-b324-8f5af9b95726",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "491 -76.52474484837771 34.73385689822422 0 0\n",
      "723 -76.47694502529616 34.93616015001211 0 1\n",
      "725 -76.3667064760517 34.814679601488194 0 1\n",
      "726 -76.25937258621086 35.09389426483537 0 1\n",
      "2636 -76.3530511940651 34.8849748269376 0 1\n"
     ]
    }
   ],
   "source": [
    "#ok, which empty tract did I miss?\n",
    "pt1 = Point(-76.2,34.5)\n",
    "pt2 = Point(-76.2,35)\n",
    "pt3 = Point(-76.4,35)\n",
    "pt4 = Point(-76.4, 34.5)\n",
    "killPoly = Polygon([pt1,pt2,pt3,pt4])\n",
    "for t in range(nTracts):\n",
    "    if tractGeom[t].intersects(killPoly):\n",
    "        x = tractGeom[t].centroid.x\n",
    "        y = tractGeom[t].centroid.y\n",
    "        print(t,x,y,tractPop[t],isSkippedTract[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "9d8fedbd-8472-4cb0-9d82-0e6f6e4e2c5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#remove this errant tract\n",
    "isSkippedTract[491]=1\n",
    "tractMAP = tractMAP.difference(tractGeom[491])\n",
    "plotPoly(tractMAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 13.852168871267956 11.335656805809036\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  10439366 5443067.0 0.5068420065378582\n",
      "compare to Census 10439388.0 Leip has Trump + Biden = 5443067.0 0.5068420065378582\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 10439388.\n",
    "atlasTrump = 2758775.\n",
    "atlasBiden = 2684292.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 10439366 10439366.0\n",
      "state Trumpers before, after slice opn are 2758775.0 2758775.000000006\n",
      "state Bideners before, after slice opn are 2684292.0 2684292.000000001\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9c8e462-1ac7-46a9-bd63-feda864d41f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = float(nTracts)**0.5 ... #trying to figure out optimal G for number of tracts and districts.  come back to this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 105 grids for 14 districts\n",
      "starting grid no 0 at x = -82.63043966666667, y = 33.95539649999999 \n",
      "starting grid no 5 at x = -82.63043966666667, y = 35.98058149999999 \n",
      "starting grid no 10 at x = -82.197551, y = 35.17050749999999 \n",
      "starting grid no 15 at x = -81.76466233333336, y = 34.360433500000006 \n",
      "starting grid no 20 at x = -81.76466233333336, y = 36.38561849999999 \n",
      "starting grid no 25 at x = -81.33177366666666, y = 35.5755445 \n",
      "starting grid no 30 at x = -80.898885, y = 34.76547049999999 \n",
      "starting grid no 35 at x = -80.46599633333335, y = 33.9553965 \n",
      "starting grid no 40 at x = -80.46599633333335, y = 35.9805815 \n",
      "starting grid no 45 at x = -80.03310766666665, y = 35.1705075 \n",
      "starting grid no 50 at x = -79.60021900000001, y = 34.360433500000006 \n",
      "starting grid no 55 at x = -79.60021900000001, y = 36.38561849999999 \n",
      "starting grid no 60 at x = -79.16733033333333, y = 35.5755445 \n",
      "starting grid no 65 at x = -78.73444166666668, y = 34.76547049999999 \n",
      "starting grid no 70 at x = -78.30155300000001, y = 33.95539649999999 \n",
      "starting grid no 75 at x = -78.30155300000001, y = 35.98058149999999 \n",
      "starting grid no 80 at x = -77.86866433333334, y = 35.1705075 \n",
      "starting grid no 85 at x = -77.43577566666667, y = 34.360433500000006 \n",
      "starting grid no 90 at x = -77.43577566666667, y = 36.38561849999999 \n",
      "starting grid no 95 at x = -77.002887, y = 35.5755445 \n",
      "starting grid no 100 at x = -76.56999833333333, y = 34.76547049999999 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 773236.7474833293 6169820.7161057 77.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 14   #NC congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if tractGeom[t].type == clipPoly.type :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if vtdGeom[p].type == clipPoly.type :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1473873280461163e-07 1.7770532499995762e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "minTractPop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 5 4.0 1 40.8 1.3786 170418.4\n",
      "7 yoyos for tract,wedge,wedgePop,r= 11 3 517135.91197427805 1.4545\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 13 4.0 3 36.8 1.154 439698.3\n",
      "I am working on tract number 20 of 2672 tracts\n",
      "I am working on tract number 40 of 2672 tracts\n",
      "I am working on tract number 60 of 2672 tracts\n",
      "I am working on tract number 80 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 81 5.0 1 41.8 1.1562 286622.5\n",
      "we have 2 non-opposing shorted wedges for tract no 88\n",
      "I am working on tract number 100 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 114\n",
      "I am working on tract number 120 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 122\n",
      "7 yoyos for tract,wedge,wedgePop,r= 124 0 199090.82957658762 1.4801\n",
      "8 yoyos for tract,wedge,wedgePop,r= 124 0 15218.693553796387 0.74\n",
      "9 yoyos for tract,wedge,wedgePop,r= 124 0 207778.67182512511 1.4962\n",
      "10 yoyos for tract,wedge,wedgePop,r= 124 0 28168.622851024935 1.1181\n",
      "10 yoyos for tract,wedge,wedgePop,r= 124 0 74129.35823649656 1.3071\n",
      "10 yoyos for tract,wedge,wedgePop,r= 124 0 118771.89010602006 1.4017\n",
      "10 yoyos for tract,wedge,wedgePop,r= 124 0 167153.61147710995 1.4489\n",
      "11 yoyos for tract,wedge,wedgePop,r= 124 0 191128.22463838372 1.4726\n",
      "12 yoyos for tract,wedge,wedgePop,r= 124 0 180730.23694548433 1.4607\n",
      "I am working on tract number 140 of 2672 tracts\n",
      "I am working on tract number 160 of 2672 tracts\n",
      "I am working on tract number 180 of 2672 tracts\n",
      "I am working on tract number 200 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 209\n",
      "I am working on tract number 220 of 2672 tracts\n",
      "I am working on tract number 240 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 247\n",
      "we have 2 non-opposing shorted wedges for tract no 253\n",
      "I am working on tract number 260 of 2672 tracts\n",
      "I am working on tract number 280 of 2672 tracts\n",
      "I am working on tract number 300 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 314\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 319 2.0 3 90.0 0.703 210981.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 319 3.0 3 90.0 0.6397 210981.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 319 4.0 3 90.0 0.5821 210981.3\n",
      "I am working on tract number 320 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 334 10.0 0 114.6 1.7605 221354.9\n",
      "I am working on tract number 340 of 2672 tracts\n",
      "I am working on tract number 360 of 2672 tracts\n",
      "I am working on tract number 380 of 2672 tracts\n",
      "I am working on tract number 400 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 401\n",
      "we have 2 non-opposing shorted wedges for tract no 402\n",
      "we have 2 non-opposing shorted wedges for tract no 408\n",
      "we have 2 non-opposing shorted wedges for tract no 411\n",
      "I am working on tract number 420 of 2672 tracts\n",
      "I am working on tract number 440 of 2672 tracts\n",
      "I am working on tract number 460 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 469\n",
      "I am working on tract number 480 of 2672 tracts\n",
      "I am working on tract number 500 of 2672 tracts\n",
      "I am working on tract number 520 of 2672 tracts\n",
      "I am working on tract number 540 of 2672 tracts\n",
      "I am working on tract number 560 of 2672 tracts\n",
      "I am working on tract number 580 of 2672 tracts\n",
      "I am working on tract number 600 of 2672 tracts\n",
      "I am working on tract number 620 of 2672 tracts\n",
      "I am working on tract number 640 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 641\n",
      "we have 2 non-opposing shorted wedges for tract no 652\n",
      "we have 2 non-opposing shorted wedges for tract no 654\n",
      "I am working on tract number 660 of 2672 tracts\n",
      "I am working on tract number 680 of 2672 tracts\n",
      "I am working on tract number 700 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 702\n",
      "we have 2 non-opposing shorted wedges for tract no 703\n",
      "we have 2 non-opposing shorted wedges for tract no 719\n",
      "I am working on tract number 720 of 2672 tracts\n",
      "I am working on tract number 740 of 2672 tracts\n",
      "I am working on tract number 760 of 2672 tracts\n",
      "I am working on tract number 780 of 2672 tracts\n",
      "I am working on tract number 800 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 808\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 810 3.0 0 83.4 0.7421 255669.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 810 4.0 0 83.4 0.6294 255669.0\n",
      "we have 2 non-opposing shorted wedges for tract no 814\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 815 3.0 0 103.7 0.9364 155051.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 815 6.0 0 103.7 2.0184 245475.3\n",
      "I am working on tract number 820 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 839\n",
      "I am working on tract number 840 of 2672 tracts\n",
      "I am working on tract number 860 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 866 3.0 1 42.4 0.89 193244.8\n",
      "we have 2 non-opposing shorted wedges for tract no 870\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 875 3.0 1 86.9 0.5955 222947.2\n",
      "I am working on tract number 880 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 883\n",
      "I am working on tract number 900 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 910 3.0 3 90.0 1.3732 187322.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 910 4.0 3 90.0 1.3682 187322.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 910 5.0 3 90.0 1.3632 187322.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 910 6.0 3 90.0 1.3583 187322.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 910 7.0 3 90.0 1.3533 187322.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 910 8.0 3 90.0 1.3484 187322.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 910 9.0 3 90.0 1.3435 187322.3\n",
      "I am working on tract number 920 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 936\n",
      "we have 2 non-opposing shorted wedges for tract no 939\n",
      "I am working on tract number 940 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 942\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 3.0 0 84.1 0.8296 296023.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 4.0 0 84.1 0.7033 296023.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 5.0 0 84.1 0.5962 296023.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 946 6.0 0 84.1 0.5054 296023.6\n",
      "we have 2 non-opposing shorted wedges for tract no 947\n",
      "I am working on tract number 960 of 2672 tracts\n",
      "I am working on tract number 980 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 981 3.0 3 40.3 0.8445 284751.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 981 4.0 3 40.3 0.731 284751.5\n",
      "I am working on tract number 1000 of 2672 tracts\n",
      "I am working on tract number 1020 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1025\n",
      "we have 2 non-opposing shorted wedges for tract no 1028\n",
      "we have 2 non-opposing shorted wedges for tract no 1037\n",
      "I am working on tract number 1040 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1048\n",
      "I am working on tract number 1060 of 2672 tracts\n",
      "I am working on tract number 1080 of 2672 tracts\n",
      "I am working on tract number 1100 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1110\n",
      "I am working on tract number 1120 of 2672 tracts\n",
      "I am working on tract number 1140 of 2672 tracts\n",
      "I am working on tract number 1160 of 2672 tracts\n",
      "I am working on tract number 1180 of 2672 tracts\n",
      "I am working on tract number 1200 of 2672 tracts\n",
      "I am working on tract number 1220 of 2672 tracts\n",
      "I am working on tract number 1240 of 2672 tracts\n",
      "I am working on tract number 1260 of 2672 tracts\n",
      "I am working on tract number 1280 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1299\n",
      "I am working on tract number 1300 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1302\n",
      "we have 2 non-opposing shorted wedges for tract no 1304\n",
      "I am working on tract number 1320 of 2672 tracts\n",
      "I am working on tract number 1340 of 2672 tracts\n",
      "I am working on tract number 1360 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1369\n",
      "I am working on tract number 1380 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1380\n",
      "we have 2 non-opposing shorted wedges for tract no 1386\n",
      "I am working on tract number 1400 of 2672 tracts\n",
      "I am working on tract number 1420 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1439\n",
      "I am working on tract number 1440 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1442\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1447 3.0 0 107.9 0.5337 143773.7\n",
      "we have 2 non-opposing shorted wedges for tract no 1450\n",
      "we have 2 non-opposing shorted wedges for tract no 1457\n",
      "I am working on tract number 1460 of 2672 tracts\n",
      "I am working on tract number 1480 of 2672 tracts\n",
      "I am working on tract number 1500 of 2672 tracts\n",
      "I am working on tract number 1520 of 2672 tracts\n",
      "I am working on tract number 1540 of 2672 tracts\n",
      "I am working on tract number 1560 of 2672 tracts\n",
      "I am working on tract number 1580 of 2672 tracts\n",
      "I am working on tract number 1600 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1610 4.0 3 36.3 1.3296 427451.4\n",
      "I am working on tract number 1620 of 2672 tracts\n",
      "I am working on tract number 1640 of 2672 tracts\n",
      "I am working on tract number 1660 of 2672 tracts\n",
      "I am working on tract number 1680 of 2672 tracts\n",
      "I am working on tract number 1700 of 2672 tracts\n",
      "I am working on tract number 1720 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1723 3.0 1 36.9 0.8928 195461.0\n",
      "I am working on tract number 1740 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1757\n",
      "I am working on tract number 1760 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1760\n",
      "we have 2 non-opposing shorted wedges for tract no 1775\n",
      "I am working on tract number 1780 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1796\n",
      "I am working on tract number 1800 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1810\n",
      "I am working on tract number 1820 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1821\n",
      "I am working on tract number 1840 of 2672 tracts\n",
      "I am working on tract number 1860 of 2672 tracts\n",
      "I am working on tract number 1880 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1882 5.0 0 104.3 1.0582 194561.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1882 6.0 0 104.3 1.0246 194561.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1882 7.0 0 104.3 0.9921 194561.0\n",
      "I am working on tract number 1900 of 2672 tracts\n",
      "I am working on tract number 1920 of 2672 tracts\n",
      "I am working on tract number 1940 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1944 4.0 3 38.9 1.0706 409597.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1948 3.0 3 36.4 0.8928 258008.2\n",
      "I am working on tract number 1960 of 2672 tracts\n",
      "I am working on tract number 1980 of 2672 tracts\n",
      "I am working on tract number 2000 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2019 4.0 2 90.0 2.2043 185346.5\n",
      "I am working on tract number 2020 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2027 5.0 1 90.0 1.5464 190271.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2027 14.0 1 90.0 1.5448 190271.5\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2027 1 190262.3297155759 1.5241\n",
      "8 yoyos for tract,wedge,wedgePop,r= 2027 1 87943.11073493298 0.762\n",
      "9 yoyos for tract,wedge,wedgePop,r= 2027 1 190271.34270488215 1.5417\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2027 1 115465.45464831882 1.1519\n",
      "10 yoyos for tract,wedge,wedgePop,r= 2027 1 150558.77146206406 1.3468\n",
      "11 yoyos for tract,wedge,wedgePop,r= 2027 1 190001.41028741415 1.4442\n",
      "12 yoyos for tract,wedge,wedgePop,r= 2027 1 181535.0771135298 1.3955\n",
      "I am working on tract number 2040 of 2672 tracts\n",
      "I am working on tract number 2060 of 2672 tracts\n",
      "I am working on tract number 2080 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2085\n",
      "I am working on tract number 2100 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2101\n",
      "I am working on tract number 2120 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2125\n",
      "we have 2 non-opposing shorted wedges for tract no 2126\n",
      "I am working on tract number 2140 of 2672 tracts\n",
      "I am working on tract number 2160 of 2672 tracts\n",
      "I am working on tract number 2180 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2193 3.0 1 38.1 0.7983 492327.7\n",
      "we have 2 OPPOSING shorted wedges for tract no 2194\n",
      "we have 2 non-opposing shorted wedges for tract no 2199\n",
      "I am working on tract number 2200 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2213\n",
      "I am working on tract number 2220 of 2672 tracts\n",
      "I am working on tract number 2240 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2253\n",
      "I am working on tract number 2260 of 2672 tracts\n",
      "I am working on tract number 2280 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2292 3.0 3 45.0 0.7216 380635.8\n",
      "I am working on tract number 2300 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2303 2.0 1 90.0 0.8384 231279.6\n",
      "I am working on tract number 2320 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2328\n",
      "I am working on tract number 2340 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2343 6.0 1 45.1 2.1091 256464.6\n",
      "we have 2 non-opposing shorted wedges for tract no 2347\n",
      "we have 2 non-opposing shorted wedges for tract no 2351\n",
      "I am working on tract number 2360 of 2672 tracts\n",
      "I am working on tract number 2380 of 2672 tracts\n",
      "I am working on tract number 2400 of 2672 tracts\n",
      "I am working on tract number 2420 of 2672 tracts\n",
      "I am working on tract number 2440 of 2672 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2458 9.0 1 45.7 1.5699 344746.3\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2458 1 344746.34449180885 1.8575\n",
      "I am working on tract number 2460 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2460\n",
      "we have 2 non-opposing shorted wedges for tract no 2467\n",
      "I am working on tract number 2480 of 2672 tracts\n",
      "I am working on tract number 2500 of 2672 tracts\n",
      "I am working on tract number 2520 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2530\n",
      "I am working on tract number 2540 of 2672 tracts\n",
      "I am working on tract number 2560 of 2672 tracts\n",
      "I am working on tract number 2580 of 2672 tracts\n",
      "I am working on tract number 2600 of 2672 tracts\n",
      "I am working on tract number 2620 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2626\n",
      "we have 2 non-opposing shorted wedges for tract no 2628\n",
      "we have 2 non-opposing shorted wedges for tract no 2637\n",
      "I am working on tract number 2640 of 2672 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2658\n",
      "I am working on tract number 2660 of 2672 tracts\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.000138413636039\n",
      "Average and max number of wedgePop loops per tract were:  6.299026946107785 25.0\n",
      "min,average,max of Home District areas were:  0.0 0.8141770508653738 2.3121531410706173\n",
      "calculated statewide vote was 0.5102144799316707, should have been 0.5068420065378582\n",
      "calcd statewide Hispanic pop was 0.08730904952775823, should have been 0.08881510819181962\n",
      "calcd statewide Black pop was 0.19621828051714452, should have been 0.2009821094753111\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.3828592814371258\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.000138413636039\n",
      "Average and max number of wedgePop loops per tract were:  6.299026946107785 25.0\n",
      "min,average,max of Home District areas were:  0.0 0.8141770508653738 2.3121531410706173 for  NC\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "#start 9:47  end 11:49\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NC 13Aug\n"
     ]
    }
   ],
   "source": [
    "date = \"13Aug\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\",\"precinctPop\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2,\"bg_density\"]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1subPtol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_subPtol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [0]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "460 0.7323437952279074 632.4988552463308 pop,GOP 0.5795078957032684\n",
      "461 0.7314570146556023 189.83502245211116 pop,GOP 0.5795078957032684\n",
      "462 0.703355013426899 810.2195292455447 pop,GOP 0.5795078957032684\n",
      "463 0.706617193914033 161.3799203871696 pop,GOP 0.5795078957032684\n",
      "465 0.7302870426550424 213.57430231472478 pop,GOP 0.5795078957032684\n",
      "466 0.7260995680235395 715.4923703541191 pop,GOP 0.5795078957032684\n",
      "486 0.7861908668262622 95.27812802820402 pop,GOP 0.8088614116434827\n",
      "1563 0.7414708834238957 1409.845886348669 pop,GOP 0.5578478964401294\n",
      "1564 0.7534404270566327 20.058658040086137 pop,GOP 0.5578478964401294\n",
      "1565 0.7460758821406569 1042.0954556112447 pop,GOP 0.5578478964401294\n",
      "1773 0.7622271078933127 757.8363172860196 pop,GOP 0.42709785335935324\n",
      "1774 0.7903984691578667 782.0822808063087 pop,GOP 0.4270978533593532\n",
      "1777 0.7975829362710821 441.15592383071424 pop,GOP 0.42709785335935324\n",
      "2043 0.7923972478136779 1169.282406427916 pop,GOP 0.673215898825655\n",
      "2044 0.7841755940826601 13.91768574847843 pop,GOP 0.6732158988256549\n",
      "2045 0.7782291332208026 404.0538639983678 pop,GOP 0.6732158988256549\n",
      "2046 0.7978163174161658 1009.310060273826 pop,GOP 0.6732158988256549\n",
      "2048 0.7886491687351812 418.8966590019096 pop,GOP 0.6732158988256549\n",
      "2128 0.6922284520594794 3.1668153622190873 pop,GOP 0.6230529595015576\n",
      "2129 0.6909864230858784 503.15090353348467 pop,GOP 0.6230529595015577\n",
      "2130 0.6951127918670849 429.3853384750113 pop,GOP 0.6230529595015577\n",
      "2131 0.6967906292932001 6.613507523743961 pop,GOP 0.6230529595015576\n",
      "2132 0.710405630148795 20.683435105541076 pop,GOP 0.6230529595015576\n",
      "2133 0.7909104469605052 447.77657537289144 pop,GOP 0.6386994088221919\n",
      "2134 0.7849988547738646 418.52075556626824 pop,GOP 0.6386994088221919\n",
      "2136 0.781243614219112 462.40448527608396 pop,GOP 0.6386994088221919\n",
      "2137 0.7689856135754042 542.8579897438203 pop,GOP 0.6386994088221919\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.8 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        plotPoly(vtdGeom[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 10\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in NC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1095027128988195 = NC std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1285748981124567 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for NC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.6499\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00337 0.51021 HD avg vs true 0.50684\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for NC\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 4.873 NC seats out of 14\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  745669.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 3.32 3.287\n",
      "fractional expected GOP seats =  8.078  out of  14 seats for NC\n",
      "simpler expected GOP seats =  8.1635  out of  14 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
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   "pygments_lexer": "ipython3",
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